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Improving student engagement using course-based social networks

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Title:
Improving student engagement using course-based social networks
Creator:
Imlawi, Jehad Mohammad
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English
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1 electronic file : ;

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Subjects / Keywords:
Social networks ( lcsh )
Social networks ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Review:
This study proposes an engagement model that supports use of course-based online social networks for engaging student, and hence, improving their educational outcomes. This research demonstrates that instructors who create course-based online social networks to communicate with students can increase the student engagement in these online social networks, and increase student perceived educational outcomes. The model is developed and tested in a higher education setting. The primary contribution of this research is deepening insights into the information systems and communication artifact by conceptualizing a model that helps researchers understand the reasons why some communication types used by instructors via a course-based social network, such as appropriate humor messages, can improve engagement among students, and improve their perceived educational outcomes, while other communication types may negatively affect engagement within this course-based social network . One other contribution is studying the moderating impact of time spent by student in the online social network, as this factor makes the studying of engagement in online setting is unique than engagement in face-to-face setting.
Thesis:
Thesis (Ph. D.)--University of Colorado Denver. Computer science and information systems
Bibliography:
Includes bibliographical references.
General Note:
Department of Computer Science and Engineering
Statement of Responsibility:
by Jehad Mohammad ImLawi.

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Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
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All applicable rights reserved by the source institution and holding location.
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862831726 ( OCLC )
ocn862831726

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Full Text
IMPROVING STUDENT ENGAGEMENT USING COURSE-BASED SOCIAL
NETWORKS
By
Jehad Mohammad Imlawi
B.A. Mutah University, 2003
M. A. Amman Arab University, 2006
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Computer Science and Information Systems
2013


This thesis for the Doctor of Philosophy degree by
Jehad Mohammad Imlawi
has been approved for the
Computer Science and Information Systems Degree
by
Judy Scott, Chair
Dawn Gregg, Advisor
Jahangir Karimi
Min-Hyung Choi
4/1/2013


Imlawi, Jehad, Mohammad. (Ph.D, Computer Science and Information Systems)
Improving Student Engagement Using Course-Based Social Networks
Thesis directed by Associate Professor Dawn Gregg
ABSTRACT
This study proposes an engagement model that supports use of course-based online social
networks for engaging student, and hence, improving their educational outcomes. This
research demonstrates that instructors who create course-based online social networks to
communicate with students can increase the student engagement in these online social
networks, and increase student perceived educational outcomes. The model is developed
and tested in a higher education setting.
The primary contribution of this research is deepening insights into the information
systems and communication artifact by conceptualizing a model that helps researchers
understand the reasons why some communication types used by instructors via a course-
based social network, such as appropriate humor messages, can improve engagement
among students, and improve their perceived educational outcomes, while other
communication types may negatively affect engagement within this course-based social
network One other contribution is studying the moderating impact of time spent by
student in the online social network, as this factor makes the studying of engagement in
online setting is unique than engagement in face-to-face setting.
The form and content of this abstract are approved. I recommend its publication.
Approved: Dawn G. Gregg
iii


DEDICATION
I lovingly dedicate this thesis to my beautiful wife, Rasha, who has been with me
through every step of this thesis, even when I couldn't be with her.
This work is also dedicated to my beloved kids, Dima, Sara, and Ahmad, "... to
the moon and back."
Finally, this work is dedicated to my parents, Mohammad and Kadijah, who
taught me the most important things I will ever learn: love of family, integrity, hard work,
persistence.
IV


ACKNOWLEDGMENTS
Though my name appears on the cover of this thesis, a great many people have
contributed to its production. I owe my gratitude to all those people who have made this
thesis possible and because of whom my graduate experience has been one that I will
cherish forever.
Foremost, I would like to express my sincere gratitude to my advisor Professor
Dawn Gregg for the continuous support of my Ph.D study and research, for her patience,
motivation, enthusiasm, and immense knowledge. Her guidance helped me in all the time
of research and writing of this thesis. I could not have imagined having a better advisor
and mentor for my Ph.D study. She believed I would accomplish this goal, even when I
didn't.
I would also like to thank the other members of my committee: Jahangir Karimi,
Judy Scott, and Min-Hyung Choi for the time and hard work they have invested in my
education.
I would like to acknowledge all the professors and instructors in University of
Colorado Denver who participated in the experiment in this thesis or allowed me to use
time of their classes to collect data for this thesis.
Finally, I would like to acknowledge all the fellow PhD students in the
Information Systems, Business School for their valuable feedback and help in our
meetings.
v


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION............................................................1
1.1 Research Problem and Scope......................................1
1.2 Topic Importance................................................3
1.3 Research Questions..............................................4
1.4 Research Approach...............................................5
1.5 Research Contribution...........................................5
1.6 The Thesis Outline..............................................6
II. LITERATURE REVIEW......................................................7
2.1 Engagement......................................................8
2.2 Relationship Building Communication............................12
2.2.1 Self-Disclosure...........................................12
2.2.2 Humor.....................................................14
2.3 Credibility....................................................16
2.4 Facebook in Education..........................................20
III. THEORETICAL BACKGROUND...............................................24
3.1 Self-Disclosure and Engagement.................................24
3.2 Humor and Engagement...........................................29
3.3 Credibility and Engagement.....................................31
3.4 Engagement and Educational Outcomes............................33
3.4.1 Impact of Engagement on Motivation to Learn...............34
3.4.2 Impact of Engagement on Satisfaction with Learning........35
3.5 Time Spent in the Online Social Network........................36
vi


3.6 Research Model
38
IV. EXPORATORY STUDY.......................................................41
4.1 Participants.....................................................42
4.2 Manipulation.....................................................42
4.3 Procedure........................................................43
4.4 Measurement......................................................44
4.4.1 Self-Disclosure.............................................46
4.4.2 Humor.......................................................46
4.4.3 Engagement..................................................46
4.5 Instrument Validation............................................47
4.6 Common Method Bias Tests.........................................48
4.7 Exploratory Study Results........................................51
V. MAIN STUDY..............................................................55
5.1 Sample...........................................................57
5.2 Experiment Design................................................58
5.3 Procedure........................................................59
5.4 Measurement......................................................59
5.4.1 Creibility..................................................61
5.4.2 Motivation to Learn.........................................62
5.4.3 Satisfaction with Learning..................................62
5.5 Main Study Results...............................................62
5.5.1 Moderating Impact of Time Spent in Online Social Networks...66
5.5.2 Post-Hoc Analysis...........................................70
5.5.3 Control Variables...........................................74
vii


VI. DISCUSSION
77
6.1 Theoretical Contribution and Implications to Research...............80
6.2 Implications to Practice...........................................82
6.3 Limitations and Future Research....................................83
6.4 Conclusions........................................................85
REFERENCES.....................................................................88
APPENDIX......................................................................107
A. Appropriate and Inappropriate Humor.......................................107
B. Examples of Facebook Pages used in the Study..............................117
C. Instructor Self-Disclosure Scale..........................................121
D. Instructor Use of Humor Scale.............................................123
E. Student Engagement Scale..................................................124
F. Instructor Credibility Scale..............................................125
G. Student Motivation to Learn Scale.........................................126
H. Student Satisfaction with Learning Scale..................................127
viii


LIST OF TABLES
Table
IV. 1 Measurement items used in the exploratory study...................... 44
IV.2 Common Method Bias Tests............................................. 50
IV.3 Sample Demographics in Exploratory Study............................. 51
IV.4 Loadings and cross-loadings.......................................... 52
IV.5 Internal consistency and discriminant validity....................... 53
IV. 6 Summary of hypotheses tests.......................................... 54
V. l University courses from which sample drawn from...................... 58
V.2 Measurement items used in the main study............................. 60
V.3 Sample Demographics.................................................. 63
V.4 Loadings and cross-loadings.......................................... 63
V.5 Internal consistency and discriminant validity....................... 65
V.6 Summary of hypotheses tests.......................................... 65
V.7 T-Test Results for Self-Disclosure ^ Engagement...................... 69
V.8 T-Test Results for Humor ^ Engagement................................ 69
V.9 T-Test Results for Credibility ^ Engagement.......................... 70
V. 10 The Classification Scheme for Posts Categories....................... 71
V. 11 Average engagement per a course-based social network................. 72
V. 12 Average engagement per an experimental group......................... 72
V. 13 Average engagement per post type..................................... 72
VI. 1 Summary of Hypotheses Tests.......................................... 78


LIST OF FIGURES
Figure
III. 1 The Research Model................................................... 39
IV. 1 The Research Model for the Exploratory Study......................... 42
IV. 3 PLS SEM Results...................................................... 54
V. 1 The Revised Research Model used in the Main Study.................... 57
V.2 PLS SEM Results Main Study......................................... 66
V.3 PLS SEM Results for Low Time Spent Group............................. 68
V.4 PLS SEM Results for high time spent group............................ 69
V.5 Average Engagement per Course-Based Social Network..................... 73
V.6 Average Engagement per Experimental Group.............................. 73
V.7 Average Engagement per Post Type....................................... 74
B. 1 A sample simulated Facebook page used in the exploratory study........ 117
B.2 A screenshot of a real Facebook page used in the main study........... 120
x


CHAPTER I
INTRODUCTION
IS researchers have become increasingly interested in understanding how to
organize and facilitate the development of online social network groups (Nambisan and
Nambisan, 2008; Wellman and Gulia, 1999, Bagozzi and Dholakia, 2002, 2006, Lin and
Lee, 2006), which is defined by Cothrel (2000) as individuals interacting virtually via
computer-mediated communications (CMC). Many reasons underlie this interest,
including the ability of a sponsor or administrator in these groups, such as instructors, to
facilitate deep and enduring affective bonds with members in these groups (Hagel and
Armstrong, 1997, Dou and Krishnamurthy, 2007).
Social network sites (such as Facebook, MySpace, and Twitter, etc.) provide the
opportunity for building and maintaining online social network groups around a specific
interest (such as an educational interest). For example, instructors in higher education can
create a course-based social network to engage students. Some of the promise and
popularity of online social networks lie in their ability to offer an alternative means to
communicate, and collaborate (Jarvenpaa et al., 2007). As such, they carry the potential
to dramatically change the ways in which we interact with one another in both real and
online world (Chaturvedi, Dolk, and Dmevich, 2011).
1.1 Research Problem and Scope
Unlike prior information technologies, the central theme in research surrounding
social network use is engagement (Hassenzahl & Tractinsky, 2006), as opposed to system
1


usablity, as defined under TAM (Davis, Bagozzi, and Warshaw, 1989). Research
suggests that complex computer mediated communication systems must be not only
usable, but engaging (OBrien and Toms, 2003). This suggests that to understand the
adoption and use of online social networks requires an examination of engagement by
users within these networks (OBrien and Toms, 2003). This represents a fundamental
shift in focus from the design of the technology itself to how the technology is used by
participants within these online social networks.
This research utilizes Communication Privacy Management theory (CPM)
(Petronio, 2002) and Instructional Humor Processing theory (IHPT) to improve our
understanding of how instructor use of self-disclosure and humor within a course-based
social network can improve student engagement. This research also utilizes social
presence theory to investigate the impact of instructor credibility on student engagement.
Then it investigates the impact of engagement on perceived educational outcomes. These
outcomes include student motivation to learn and satisfaction with learning. The
moderating impact of time spent in the online social network by the student is
investigated by this study as well. While higher education is not a traditional business
environment, students in higher education are typically the early adopters for the newest
technologies available and using higher education for this study may provide insights into
how social networks can transform interaction in other domains as well (as called for by
Benbasat & Zmud, 2003; Agarwal & Lucas, 2005; DeSanctis, 2003, and King &
Lyytinen, 2004).
2


1.2 Topic Importance
The role social networks play in higher education is gaining increased attention with the
rise of massively open online courses (MOOCs). In fact, many higher education leaders
see MOOCs as the future of higher education (Schaffhauser, 2012). These systems rely
on online social networks to create connectedness and to improve engagement. MOOCs
use social networks to create and sustain the social dimension of learning, and to enhance
knowledge production rather than simply providing a platform for knowledge
consumption (Bousquet, 2102). Yet very little is known about the types of messages that
are appropriate to be shared between instructors and students in these communities.
Most research on instructor student interaction conducted to date has been in face-to-face
environments; little previous research has studied their impacts in online environments,
like Facebook. Thus, there is a need to better understand how communication between
instructor and students can be enhanced through the use of social network tools.
It is possible that what is true in face-to-face environments may not be true in
online environments. First, in online environments, students are more likely and have
more opportunity to spend more time interacting with other students and the instructor,
which is not necessarily true in face-to-face environments. This raises the contextual
condition that says why the studied relationships in this research could be stronger or
weaker in online environments, and may not be the same as in face-to-face environments.
Second, online environments have fewer cues about how to interpret messages. For
example, the use of humor online could potentially pose problems because people often
rely on environmental cues when deciding how to interpret a humorous message
(Leventhal & Cupchik, 1976). Third, Child & Petronio (2010) have found that
3


individuals have different perceptions and rules for face-to-face self-disclosure than they
have for self-disclosure in online social networks. For example, their research suggests
that individuals tend to reveal more private information online (Child & Petronio, 2010).
Fourth, Research also suggests that students may perceive an instructor use of Facebook
as an attempt to foster positive relationships with students, and perceive the instructor as
more available to students, which may improve perceptions of instructor credibility
(Mazer, Murphy, and Simonds, 2007; OSullivan, Hunt, and Lippert, 2004), and hence
improve student engagement. Accordingly, student expectations in online environments
are likely to be different than in face-to-face environments. The discrepancy between
engagement online and offline highlights the need for research on engagement in online
social networks. This thesis addresses the critical need for an improved understanding of
what messages are most effective in social networks used to support education.
1.3 Research Questions
This thesis is motivated by the following questions:
What types of communication messages can be used by instructors, via a course-
based social network, to improve student engagement within this social network?
How does instructor credibility impact student engagement in a course-based
social network?
How does engagement, in a course-based social network, impact educational
outcomes, like motivation to learn and student satisfaction with learning?
Does the amount of time the student spends in the online social network moderate
the impact of instructor credibility, instructor self-disclosure, and instructor use of humor
on engagement?
4


1.4 Research Approach
This research utilizes Facebook pages and groups to provide instructors with the
opportunity to communicate directly with students on the most popular social network
platform without requiring the instructor to friend students, or to have access to their
private profiles. The thesis includes two studies. The first study involves an exploratory
study that utilizes a survey to investigate the best combination of communication types
that can be used by instructors via a course-based social network (Facebook page or
group). The second study involves a real-world experiment, where an instructor
communicates with students via a course-based social network group for an entire
semester. The experiment includes two groups. In the test group the instructor
communicates using the most effective communication types identified in the exploratory
study. In the control group the instructor only posts messages related to course content
and school announcements via the Facebook page or group. The difference in the
outcomes between the two groups is then measured using a survey and by recording the
actual engagement. The unit of analysis is at the individual level as perceptions of student
engagement and individual education outcomes are considered. Structural equation
analysis is used to examine the proposed hypotheses and test for significant differences
between groups. A post-hoc analysis is also conducted to examine differences in actual
student engagement (likes and comments) between the two experimental groups.
1.5 Research Contribution
This research is significant because it utilizes Communication Privacy
Management theory (Petronio, 2002) and Instructional Humor Processing theory to
5


expand our understanding of instructor self-disclosure and use of humor via a course-
based social network. It also utilizes social presence theory to investigate the impact of
instructor credibility on student engagement. The research also contributes to the theory
by providing an engagement model that is unique to online educational setting, by
utilizing Moores transactional distance theory, to study the moderating impact of time
spent by student in the online social network.
1.6 The Thesis Outline
The focus of this thesis is on engagement in course-based social networks.
Chapter two will review the previous literature that has investigated our research
problem. In chapter three we develop our research model and hypotheses. In chapter four,
we present the exploratory study. In chapter five, we present the main study. In chapter
six we discuss and interpret the results of our analysis. In this chapter we also conclude
the thesis, present a discussion of the research contributions and implications for theory
and practice, and discuss the study limitations and opportunities for future research.
6


CHAPTER II
LITERATURE REVIEW
Computer-mediated communication (CMC) is defined as the process by which
people create, exchange, and perceive information using networked telecommunications
systems (or non-networked computers) that facilitate encoding, transmitting, and
decoding messages (December, 1996). CMC examples include e-mail, forums, chat, and
online social networks (Herring, 2002). CMC modes have transformed organizational
culture and interaction (Abbasi and Chen, 2008).
CMC has provided invaluable support for various business operations including
organizational communication, knowledge dissemination, transfer of goods and services,
and product reviews (Turney and Littman, 2003; Cothrel, 2000). CMC has enabled online
social network groups (Wenger and Snyder, 2000), virtual teams and group support
systems (Abbasi and Chen, 2008), and networks of practice (Wasko and Faraj, 2005).
These systems enabled companies to tap into the wealth of information and expertise
available across corporate lines, and facilitate organizational operations regardless of
physical boundaries (Fjermestad and Hiltz, 1999; Montoya-Weiss, Massey, and Song,
2001). However, little research has investigated the potential impact of CMC on
engagement in online social networks. In this section, the current literature on
engagement is discussed. The current literature on communication that has the potential
to build and support relationships is then discussed, followed by a review of literature on
credibility. Finally, the current literature on using Facebook in education is discussed.
7


2.1 Engagement
Engagement with information technologies and systems is the feeling that a is the
feeling that a system has caught, captured, and captivated user interest (Jacques, Preece,
and Carey, 1995). System use, defined as the frequency, duration, and intensity of an
employees interactions with a particular system (Venkatesh et al., 2003, Davis et al.
1989), is similar to involvement which may occur because of task demands or deadlines
and thus may not be enjoyable (Sandelands and Buckner, 1989). In contrast, engagement
includes intrinsic interest. Therefore, there have been calls for research that help to create
systems for which users interactions are pleasurably engaging, fun, and intrinsically
motivating (Laurel, 1991; Malone and Lepper, 1987), to design systems to be more
lively, intriguing, or fascinating (Giardina, 1992), and to recognize the achievement of
engagement as an important goal in the design of systems (Mayes, 1992). Moreover, in
the past few decades, human-computer interaction studies have emphasized the need to
move beyond usability to understand and design for more engaging experiences
(Hassenzahl and Tractinsky, 2006; Jacques et al., 1995; Laurel, 1993).
Engagement is considered a desirable -even essential- human response to
computer-mediated activities (Laurel, 1993, p. 112). A Web interface that is boring, a
multimedia presentation that does not captivate users attention or an online forum that
fails to engender a sense of community is quickly dismissed with a simple mouse click
(OBrien, 2008). Failing to engage users equates with no sale on an electronic commerce
site and no transmission of information from a website. People go elsewhere to perform
their tasks and communicate with colleagues and friends (OBrien, 2008). Engaging
interactions are sought after by both users and developers of computer systems and
8


applications. Given the increased emphasis on user experience, it is no longer sufficient
to ensure that technologies are merely usable (Blythe, Overbeeke, Monk, and Wright,
2003). Successful technologies must engage users.
In face-to-face educational settings, student engagement was conceptualized as a
psychological process, specifically, the attention, interest, investment, and effort students
expend in the work of learning (Marks, 2000, P 154,155). There is a general agreement
that engagement in learning is important for success (Klem and Connell, 2004) and is
clearly an important component of the student experience. Research shows that student
engagement in educational activities is positively related to learning, personal
development and educational effectiveness (Klem and Connell, 2004). Research also
links higher levels of engagement in school with improved performance. For example,
researchers have found student engagement a robust predictor of student achievement and
behavior in school (Voelkl, 1995; Finn, 1993; Arhar and Kromrey, 1993; Mounts and
Steinberg, 1995). Students engaged in school are more likely to earn higher grades
(Goodenow, 1993; Willingham, Pollack, and Lewis, 2002) and test scores, (Willingham,
Pollack, and Lewis, 2002; Roderick and Engle, 2001) and have lower drop-out rates.
(Connell, Halpcm-Kelsher, Clifford, Crichlow, and Usinger, 1995) In contrast, students
with low levels of engagement are at risk for a variety of long-term adverse
consequences, including disruptive behavior in class, absenteeism, and dropping out of
school (Steinberg, Brown, and Dombusch, 1996; Finn, 1989; Lee, Smith, and Croninger,
1995).
The limited evidence to date about the relationship between student use of
technology and student engagement, mostly from face-to-face educational settings, have
9


affirmed the utility of information technology on: promoting student engagement (Hu and
Kuh, 2001; Nelson Laird and Kuh, 2005; Robinson and Hullinger, 2008), and affecting a
variety of outcomes such as student self-reported gains in general education, personal
development, and intellectual development (Hu and Kuh, 2001; Kuh and Hu, 2001; Kuh
and Vesper, 2001). However, some studies show mixed results. For example, Alavi
(1994) and Oblinger and Maruyama (1996) provided evidence that educationally
purposeful use of information technology, such as e-mailing instructors or other students
about assignments, does encourage collaboration among students. Chen, Lambert, and
Guidry (2010) found a positive relationship between Web-based learning technology use
and student engagement and desirable learning outcomes. They found that students who
utilize the Web and Internet technologies in their learning tend to score higher in the
traditional student engagement measures. Similarly, Robinson and Hullinger (2008)
found that asynchronous instructional technology provide students with more time to
think critically and reflectively, which in turns stimulates higher order thinking such as
analysis, synthesis, judgment, and application of knowledge. At the same time, Reisberg
(2000) suggests that uses of information technology may distract students from
participating in empirically confirmed effective educational practices.
Research by Atkinson and Kydd (1997); Dyck and Smither (1994) and Whitley
(1997) investigated student engagement in the online educational environment, and have
shown that experience with information technologies is associated with student
engagement. This experience has been associated with spending more time in the online
educational environment (Hiltz, 1994; Ridley and Sammour, 1994). This suggests that
10


students who spend more time in the online educational environment are more likely to
be engaged in and satisfied with their own learning experience.
In online education, research has found that for learning to take place, online
presentations should engage their audiences (Webster and Ho, 1997; Jacques et al.,
1995), and educators should critically engage students with technology (Salvo, 2002).
Although engagement represents an important issue faced by instructors when
communicating online with students, little empirical research has addressed how best to
improve student engagement in an online setting (Mallon and Webb, 2000). Similarly,
while the number of college courses being delivered via the internet is increasing rapidly,
our knowledge of what makes these courses effective learning experiences that engage
students is still limited (Arbaugh, 2000). The evaluation of online learning needs to go
beyond traditional measures of students knowledge and learning and consider the quality
of the learning experience as a whole (Robinson and Hullinger, 2008). Measures of
student engagement offer such an evaluation.
There are numerous differences between online settings and face-to-face
classrooms which can impact engagement. In online setting students are more likely and
have more opportunity to spend more time interacting with other students and the
instructor. However, educators also report challenges engaging students in online work
(Andrew Miller, 2012, Joanne M. Kossuth, 2011). The fundamental differences that exist
between online interaction and face-to-face interaction suggest there is a need to better
understand how engagement can be improved in online settings. One way to build
engagement in online setting is through the use of communication that builds and
improves interpersonal and professional relationships.
11


This study investigates the nature of student engagement in online educational
environment to answer the questions of what promotes engagement in the online
environment when using specific technology, online social networks, rather than studying
the impact of using information technology in general.
2.2 Relationship Building Communication
The role of communication in relationship building is crucial (Berger &
Calabrese, 1975), and it is an essential part of building engagement in online social
networks (Kodish & Pettegrew, 2008).
Uncertainty Reduction Theory (URT) (Berger & Calabrese, 1975) presumes that
the beginning of interpersonal relationships is fraught with uncertainties, and people want
to reduce uncertainty in relationships through knowledge and understanding.
Communicating directly with a person is one way to learn about each other and reduce
uncertainty in relationships (Berger & Calabrese, 1975). This suggests that
communications increases knowledge about others, reduces uncertainty in relationships,
and hence, builds relationships and engagement among members of online groups. Two
types of communication commonly used when building relationships are self-disclosure
and humor. These two types of communication were selected after looking at some real
course-based social networks used by instructors to communicate with their students,
mainly via Facebook and twitter.
2.2.1 Self-Disclosure. Wheeless & Grotz (1976) defined the self-disclosure
construct as any message about the self that a person communicates to another.
Research in face-to-face environments suggests that people have higher satisfaction and
12


feelings of trust and solidarity when they have relationships with higher levels of self-
disclosure (Wheeless, 1976, 1978; Wheeless & Grotz, 1977; Martin & Anderson, 1995;
Martin, Anderson, & Mottet, 1997, 1999).
Social Penetration Theory (SPT) (Altman and Taylor, 1973; Taylor, 1968; Taylor and
Altman, 1975; Shaw and Costanzo, 1982) suggests that relational closeness and
interpersonal communication progress from superficial to intimate as relationships
develop. It suggests that closeness develops through self-disclosure (Taylor and Altman,
1975). Self-disclosure stimulates feedback. The quality of the feedback is related to the
amount and relevance of self-disclosure we receive and share with others. Self-disclosure
increases with the need to reduce uncertainty in a relationship.
Communication privacy management (CPM) theory (Petronio, 2002) offers a privacy
management system that identifies ways privacy boundaries are coordinated between and
among individuals (Petronio, 2002, p. 3) and suggests a way to understand the tension
between revealing and concealing private information (Petronio, 2007, p. 218) between
and among those individuals. CPM is an evidence-based theory about how people
manage private information disclosure. CPM asserts that there are relational and personal
needs, like engaging others, that are met by giving access or revealing private
information.
Self-disclosure has both benefits and risks (Metzger, 2007). The benefits of disclosing
private information include self-expression, social control, and the potential for
improving interpersonal relationships (Petronio, 2002; Taylor and Altman, 1975). The
risks may include loss of face, status, control, or credibility (Metzger, 2007). CPM theory
states that individuals develop rules to help them maximize the benefits of disclosure.
13


These rules help individuals decide what, when, and whom to disclose (Petronio, 2002).
However, CPM theory does not explain what factors make disclosure is associated with
risk or associated with benefits. The relevance of self-disclosure has an impact on
benefits and risks of self-disclosure, and hence on interpersonal relationships (Taylor and
Altman, 1975).
CPM has been utilized to explain self-disclosure issues in personal relationships
(e.g., Caughlin & Afifi, 2004; Mazur & Ebesu Hubbard, 2004). (Rawlins, 2000)
contended that the balance between self-disclosure and concealing of private information
is especially important when considering the classroom context. Principles of CPM
theory can be utilized to investigate instructor privacy management in the classroom
context, where instructor-students relationship is public, yet instructors do disclose some
private information.
2.2.2 Humor. Humor is defined as communication that involves multiple,
incongruous meanings that are amusing in some manner (Gervais and Wilson, 2005). S.
Booth-Butterfield and Booth-Butterfield (1991) emphasized the intentional use of both
verbal and nonverbal communication behaviors that elicit positive responses like laughter
and joy in their definition of humor.
Research provides some evidence that humor can be used appropriately in the
classroom to enhance learning and student perceived learning outcomes. However, other
research has demonstrated a negative impact of humor on learning (e.g., Harris, 1989;
Stuart & Rosenfeld, 1994; Torok, McMorris, & Lin, 2004; Ziv, 1988). These conflicting
results may be due to differences in the experimental procedures. For example one study
asked students to recall a class environment where humor has been used (Gorham &
14


Christophel, 1990). Other studies used an artificial experimental setting (Ziv, 1988).
Another possible cause for differences in past experimental results may be that different
types of humor were used or that the delivery mechanism for the humor differed. For
example, some studies have introduced humor through instructor lectures, cartoons, or
audiotapes (Banas, Dunbar, Rodriguez & Liu, 2011).
Research by Baxter & Wilmot (1984) and Graham (1995) indicated that a sense
of humor facilitates the reduction of uncertainty in interpersonal relationships and also
serves to reduce social distance between interactants, and hence improves their
engagement in a community. Humor is important in a variety of settings, including the
development of social relationships (Alberts, 1990; Baxter, 1992). Humor is an engaging
personality trait that has direct implications on building interpersonal and professional
relationships and communication (Graham, 1995). People at all relationship stages
identify humor as a key factor in communication satisfaction (Hecht, 1984), and
relationship maintenance (Canary, Stafford, Hause, & Wallace, 1993).
Incongruity theory provides evidence as to why humor is useful in relationship
building especially in educational settings. Incongruity theory suggests that people find
something humorous when they are required to resolve incongruities in the message
(Berlyne, 1960; Suls, 1972). The processing of these humorous incongruities can lead to
a cognitive shift resulting from the sudden solution to the problem posed (Latta, 1999;
Brian Boyd, 2004). The enjoyment gained from successfully resolving humor can lead to
beneficial outcome.
Research by Kurtzberg, Naquin, and Belkin (2009) demonstrates that the use of
humor in communication results in increased trust and satisfaction levels, higher joint
15


gains for the community, and higher individual gains for the community member who
initiated the humorous event. This suggests that the individual who initiates humor is
engaging others in the community, and improving their joint gains and individual
outcomes.
2.3 Credibility
Credibility refers to the attitude of a receiver which references the degree to
which a source is seen to be believable (McCroskey, 1998, p. 80). Instructor credibility,
which is one of the most important variables affecting the instructor-student relationship
(Myers, 2001), is defined as the degree to which an instructor is perceived to know what
he or she is talking about, the degree to which the instructor is perceived as honest, and
the degree to which the instructor is perceived as to have the students best interests in
mind (McCroskey and Teven, 1999). Researchers have identified instructor credibility as
a critical factor in the learning process, the higher the credibility, the higher the
learning (Thweatt & McCroskey, 1998, p. 349). Research shows that perceived
instructor credibility matters to instructors and students alike (Obermiller, Ruppert, and
Atwood, 2012; Lavin, Davies, and Carr, 2010).
Prior studies on instructor credibility have found when instructors are viewed as
credible sources of knowledge and academic support, several important classroom
outcomes are enhanced, including, but not limited to, learning (Frymier and Thompson,
1992; Martin, Mottet, and Chesebro, 1997; McCroskey, Valencic, and Richmond, 2004;
Schrodt et al., 2009), student motivation to learn (Frymier and Thompson, 1992; Martin,
Mottet, and Chesebro, 1997), communication between the instructor and student, both in
16


and out of the classroom (Myers, 2004; Myers and Bryant, 2004), perceived teaching
effectiveness (Myers, 2004), student perceptions of cognitive learning and affective
learning (Johnson & Miller, 2002; Russ, Simonds, & Hunt, 2002; Teven & McCroskey,
1997), instructor affinity seeking behaviors (Frymier & Thompson, 1992), instructor
assertiveness and responsiveness (Martin, Chesebro, & Mottet, 1997), teacher immediacy
(Thweatt & McCroskey, 1998), perceived instructor argumentativeness (Schrodt, 2003),
and affect for the course and instructor (McCroskey et al., 2004).
Students who consider their instructors to be credible recommend these
instructors to their friends (Nadler & Nadler, 2001), feel understood by their instructors
(Schrodt, 2003), evaluate both the class and their instructor more positively (Schrodt,
2003; Teven & McCroskey, 1997; Lavin, Davies, and Carr, 2010), are generally more
satisfied (Obermiller, Ruppert, and Atwood, 2012), and are likely to take additional
courses from them (Nadler and Nadler, 2001). Lavin, Davies, and Carr (2010) found
credibility to have impacts on the students preparation for each class, attentiveness,
appreciation for instructor effort, and respect for the instructor (Martinez-Egger and
Powers, 2002).
Nearly two decades ago, Frymier and Thompson (1992) noted that there was little
research offering instructors advice on how to increase their credibility in the classroom,
which established a new direction in research studying instructor credibility.
Consequently, instructional communication researchers have devoted substantial efforts
toward addressing the issue of offering instructors advice on how to increase their
credibility. Some investigators have focused primarily on instructor characteristics and
communication behaviors that enhance credibility (e.g., Edwards & Myers, 2007; Martin,
17


Chesebro, & Mottet, 1997; McCroskey, Valencic, & Richmond, 2004; Myers, 2001;
Schrodt, 2003; Schrodt, Turman, & Soliz, 2006; Teven, 2001; Semlak & Pearson, 2008),
and instructor behaviors (McCroskey et al., 2004). In particular, instructors who use
argumentative messages (Schrodt, 2003), verbal and nonverbal immediacy behaviors
(Johnson & Miller, 2002; Teven & Hanson, 2004), affinity-seeking behaviors (Frymier &
Thompson, 1992), appropriate levels of technology use (Schrodt & Turman, 2005;
Schrodt & Witt, 2006), who are assertive and responsive (Martin, Chesebro, & Mottet,
1997), who use nonverbal immediacy cues (McCroskey et al., 2004; Teven & Hanson,
2004), and who engage in out-of-class communication with their students (Myers, 2004)
are generally perceived as being more credible in the classroom. Other variables affecting
instructor credibility include how the instructor dresses (Morris, Gorham, Cohen, &
Huffman, 1996), the instructional format of the course (Todd, Tillson, Cox, &
Malinauskas, 2000), the aesthetic appeal of the instructors office (Teven & Comadena,
1996), and the instructors sex, race, age, and ethnicity (Hendrix, 1998; Patton, 1999;
Semlak, Pearson, 2008). Conversely, instructor credibility is inversely associated with
instructor misbehavior (Thweatt & McCroskey, 1998) such as perceived instructor verbal
aggressiveness (Myers, 2001; Schrodt, 2003).
Instructor credibility has been found to mediate the effects of instructors
prosocial communication behaviors on students learning outcomes (Schrodt et al., 2009),
mediate instructors classroom communication behaviors (nonverbal immediacy,
enthusiasm, and homophily) and students intentions to persist in college (Wheeless,
Witt, Maresh, Bryand, and Schrodt, 2011). Instructor credibility has been also found to
fully mediate the effects of immediacy and partially mediate the effects of instructor
18


confirmation and clarity on learning outcomes (Schrodt et al., 2009). Enhanced
credibility not only functions as a positive outcome of effective classroom instruction but
also mediates the effects of instructor behaviors to student and classroom outcomes (Finn
et al., 2009).
Current research has focused on instructor credibility as both a product of
instructor behaviors and as an antecedent to student learning outcomes in the college
classroom (McCroskey et al., 2004). The concept and aspects of perceived credibility, its
importance to the teaching experience, and the specific importance of communication
behaviors in credibility impressions, have received substantial attention in instructor
credibility research. However, little attention has been devoted to the role instructor
credibility can play in engaging students especially in online educational environments.
When studying instructor credibility in an online context, there are several factors
that can impact students credibility perceptions. Previous studies (Fogg, 2002; Fogg and
Marshall, 2001; Fogg and Tseng, 1999; Fogg, Marshall, Laraki, Varma, Fang, Paul,
Rangnekar, Shon, Swani, and Treinen, 2001; Johnson and Wiedenbeck, 2009) show that
providing information about the author of online information as well as a picture
enhances credibility. The types of information the instructor shares online can also
impact credibility perceptions. Johnson (2011) examined whether posting social,
scholarly, or a combination of social and scholarly information to Twitter has an impact
on the perceived credibility of the instructor. She found that participants who viewed only
the social tweets rated the instructor significantly higher in perceived credibility than the
group that viewed only the scholarly tweets. Myers, Brann, & Members of Comm 600
(2009) examined how college students consider their instructors to establish and enhance
19


their credibility through their in-class self-disclosure. Witt and Kerssen-Griep (2011)
investigated the combined effects of face-threat mitigation and instructor nonverbal
immediacy on perceived instructor credibility.
Today, the communicative interaction opportunities between instructors and
students are broadened, mainly through online social networks. Instructors report
anecdotally that these technologies have increased the time, frequency, and breadth of
instructor-student communication (Jacobs, 2004; Menzies & Newson, 2007; Osterlund &
Robson, 2009). A generation ago, most communication occurred in the classroom or in
office hours (Obermiller, Ruppert, and Atwood, 2012). Computer-mediated
communication, like online social networks, has increased the time available for
interacting among students, and between students and the instructor. Computer-mediated
communication demands an expansion of our understanding of instructor credibility in an
online educational environment, and its impact on student engagement in such an
environment.
2.4 Facebook in Education
Social networks sites (such as Facebook, MySpace, Twitter, etc.) are Internet-
based CMC. Social networks sites are web-based services that allow individuals to (1)
construct a public or semi-public profile within a bounded system, (2) articulate a list of
other users with whom they share a connection, and (3) view and traverse their list of
connections and those made by others within the system (Boyd and Ellison, 2007). Most
of the online interactions via these social network sites were found to be between people
who have also talked on the telephone or met face-to-face, real life friends or colleagues
20


(Miller & Slater, 2000). One domain where social network sites are increasingly
important is higher education. Social network sites allow students and their instructor to
enhance their face-to-face interaction. Social network sites can be used by instructor to
engage students. Researchers argue that social network systems should be a part of the
classroom experience to support education communication, interaction, and relationships
(Bosch, 2009).
Facebook is the dominant social network site used in education. It is distinctive
from all other social network systems because it has stronger roots in the academic
community (Downes, 2007), and is the largest social network site (Raphael, 2009).
Students typically check their Facebook accounts much more often than their
schools online course administration software, even when this software provides chat-
rooms and discussion boards for synchronous and asynchronous online discussions
(Bosch, 2009). This suggests that it is not only the functionality provided by Facebook
that drives interaction, it is also the acceptance of such a network. Students even check
their email and Facebook with approximately equal frequency (Roblyer, McDaniel,
Webb, Herman, & Witty, 2010) which indicates the degree to which Facebook is
integrated into student daily lives.
Facebook is providing the opportunity to enhance the out-of-class
communication. Out-of-class communication is defined as instructor-student
communication, occurring outside of the classroom setting that demonstrates
responsiveness to students needs (Jones, 2008, p. 375). These interactions are often
voluntary, yet most students report having some amount of out-of-class communication
21


with their instructors (Fusani, 1994; Jaasma & Koper, 1999). Out-of-class
communication has been found to be positively related to student motivation (Knapp &
Martin, 2003; Jaasma & Koper, 1999). Waldeck, Kearney, and Plax (2001) found that
students are more likely to communicate with those instructors online who utilize
immediacy behaviors (e.g., use humor in classroom, or use Facebook in communication
with students). The more students involve and get feedback on their learning activities, or
problem solving, the more adept they should become (Kuh, 2003; Shulman, 2002).
Researchers have investigated whether Facebook and social networks sites pose a
distraction from academic pursuits rather than a conduit towards educational goals
(Selwyn, 2009). Research has examined how students feel about having contact with their
instructors on Facebook, and how this contact influences student perceptions of their
instructors (Hewitt & Forte, 2006; Mazer, Murphy, & Simonds, 2009). The developing
opportunities that Facebook can provide for education caused Facebook.com to ask
developers to build new educational platforms to provide collaboration and connections
tools in classrooms (Morin, 2007). This is in addition to the pages and groups that are
already available in Facebook which can be used to support university courses.
Mazer et al. (2007) compared Facebook to university-housed discussion boards
and found that student interaction on Facebook is high, while interaction on a typical
university discussion boards is more limited. This may be because university discussion
boards are mostly static or because students expect a more professional website when
they use the university-housed discussion boards. Studies comparing student interaction
rate on Facebook comparing to course management systems found that students preferred
interaction on Facebook over the interaction on classic course management systems
22


(Kosik, 2007; Stutzman, 2008). Both Stutzman (2008) and Kosik (2007) reported that
student preference for Facebook primarily stems from their familiarity and experience
with Facebook, as well as from the immediate response they get when they need help.
Studies investigating instructor motivations for using Facebook over other course
management systems found a desire to meet students at their spaces and to break down
barriers between themselves and students. Other research encouraged instructors to
integrate Facebook into their university courses to foster critical thinking and allow
students to create connections among their peers (Barnes, Marateo, and Ferris, 2007).
23


CHAPTER III
THEORETICAL BACKGROUND
Initial research on the use of online social networks in education suggests they
can be helpful. However, research has not addressed how instructors may best use these
tools to engage students and improve their educational outcomes. This study examines
how self-disclosure (both course related and unrelated), the use of humor via a course-
based social network, and instructor credibility can be used to improve student
engagement, as well as to improve perceived educational outcomes, like student
motivation to learn, and satisfaction with learning.
Engagement is the feeling that a system has caught, captured and captivated user
interest (Jacques et al., 1995). Student engagement in a course-based social network
means this course-based social network keeps the student totally absorbed in the
browsing of content of this social network, holds the student attention, excites the student
curiosity, is fun, is intrinsically interesting, and is generally engaging.
3.1 Self-Disclosure and Engagement
One type of communication that can be used in a course-based social network by
instructors is self-disclosure. Research suggest that when instructors personalize their
teaching by talking about themselves, and telling stories (Nussbaum, Comadena, &
Holladay, 1987), it leads to improvements to the clarity of the information presented for
students (Downs, Javidi, and Nussbaum, 1988; Wambach and Brothen, 1997), which held
the student attention, improvements to the student perceptions of affective learning
(Sorensen, 1989), which excited the student curiosity, and improvements in the students
24


perceptions of their instructors ability to explain course content (Andersen, Norton, &
Nussbaum, 1981; Bryant, Comiskey, Crane, & Zillman, 1980; Bryant, Comiskey, &
Zillman, 1979; Civikly, 1986; Norton & Nussbaum, 1981), which make absorbing these
course content is intrinsically interesting. Holding the student attention, exciting the
student curiosity, and the feeling of intrinsically interesting when absorbing course
content are components of student engagement.
Fusani (1994) claimed that instructor self-disclosure is a rich personal source of
student-teacher communication. Cayanus (2004) argued for the use of instructor self-
disclosure as an effective instructional tool to foster student learning and make it
intrinsically interesting. Gorham (1988) and McBride & Wahl (2005) contend that
instructor self-disclosure behavior is a strategy that instructors can use to create an
immediate classroom environment that encourages students participation, and attract
student attention. Self-disclosure used during out-of-class communication allows for the
disclosures to be more personalized and directly related to student's problems (Fusani,
1994). Communication behaviors of instructors, like their self-disclosure via a course-
based social network, influence student motives for communicating within the classroom
(face-to-face community) and out-of-classroom (via the course-based social network),
and increase student tendencies to communicate (Cayanus, Martin, and Weber, 2003;
Myers, Mottet, & Martin, 2000; Mottet, Martin, & Myers, 2004), by making these
communication are perceived as more interesting by the student.
Prior research has investigated the impact of self-disclosure on individual
students educational outcomes (e.g. Mazer, Murphy, and Simonds, 2007). However, it
has only investigated the impact of the quantity of instructor self-disclosure on
25


educational outcomes, and not the impact of different types of self-disclosure. Past
research in face-to-face environments indicates that the impact of instructor self-
disclosure is dependent on more than just the amount of self-disclosure made (Lannutti
and Strauman, 2006). It suggests that types of information disclosed will have different
impacts (Cayanus & Martin, 2008). This study extends prior research by examining the
impact of two types of instructor self-disclosure via a social network: self-disclosure
about instructor private information related to the course, e.g., work experience, and self-
disclosure about the instructors private information unrelated to the course, e.g., personal
life and beliefs.
Communication privacy management theory (CPM) suggests that the decisions
about whether and when to disclose private information is rule-based (Petronio, 2002).
These rules are formed based on a variety of criteria, including culture, gender,
contextual factors, risk-benefit ratio, and motivations. The same rules could be utilized by
instructors, intentionally or unintentionally, to manage their private information self-
disclosure. For instance, instructors may employ a motivation rule to evaluate their desire
to engage students in the course-based social network and to improve student educational
outcomes. However, a risk-benefit ratio rule also governs the instructor self-disclosure.
For example, disclosing about work experiences related to the course may have a
different risks and benefits than disclosing about beliefs unrelated to the course. CPM
theory states that individuals develop rules to help them maximize the benefits of
disclosure. These rules help individuals decide what, when, and whom to disclose
(Petronio, 2002). However, CPM theory does not explain what disclosure factors are
associated with risk or associated with benefits. The type and relevance of self-disclosure
26


has an impact on benefits and risks of self-disclosure, and hence on interpersonal
relationships (Taylor and Altman, 1975).
Researchers investigating self-disclosure in traditional classrooms found that it
can create an environment that encourages student participation (Goldstein & Benassi,
1994). However, researchers have also found that certain topics should be avoided by
instructors. Nunziata (2007) reported that an instructor's personal problems, personal
opinions, and alcohol consumption are viewed by students as inappropriate forms of
instructor self-disclosure, which could negatively affect perceptions of instructor
credibility, and make communication with this instructor is less interesting. Lannutti &
Straumann (2006) argued that instructor self-disclosure should not muddy the
professional boundary between the instructor and the student, or hold their attention off
learning. Finally, research by Chaikin and Derlega (1974) suggests that self-disclosure of
intimate and private information to a stranger, self-disclosure to an acquaintance, and
self-disclosure to someone of a different age or position is less appropriate and more
maladjusted than nondisclosure.
Online communication has been shown to have higher levels of self-disclosure
than seen in face-to-face communication (Des Jarlais et al., 1999; Epstein, Barker, &
Krotil, 2001; Lessler, Caspar, Penne, & Barker, 2000). Researchers have found that there
can be an online dis-inhibition effect that allows some people to self-disclose more
frequently or more intensely than they would in person (Suler, 2004). There are a number
of characteristics of online communication that can lead to increased sharing of personal
information. One factor that has been found to increase self-disclosure online is the
relative anonymity associated with online communication (Sobel, 2000; McKenna and
27


Bargh, 2000, Joinson, 2001, Joinson, 2003). Another characteristic of online
communication that can encourage enhanced information sharing is the fact that there are
reduced nonverbal cues when communicating online (Suler, 2004; Walther, 1996). The
fundamental differences between face-to-face communication and online communication
have led to differing sets of privacy rules for the two different communication types. In
fact, researchers have found that there are two different sets of norms (or privacy rules)
governing offline and online self-disclosure and these norms are unrelated (Mesch and
Baker, 2010). This is consistent with prior research investigating instructor
communication via Twitter. Researchers found that instructors that post social
information to twitter are perceived by students as being more credible than those who
post more scholarly content (Johnson, 2011).
Instructor self-disclosure to students via a course-based social network is a form
of professional communication which can have higher risks related to information
sharing than most social communication types. Accordingly, communication privacy
management theory suggests that personal information should only be shared if there is a
direct benefit to students that outweighs these risks. Thus the following hypotheses are
proposed:
HI: Self-disclosure via a course-based social network about topics directly related
to the course will have a positive impact on student engagement in the course-based
social network.
28


H2: Self-disclosure via a course-based social network about topics unrelated to
the course will have a negative impact on student engagement in the course-based social
network.
3.2 Humor and Engaement
Gorham and Christophel (1990) identified humor in academic context as an
important immediacy behavior that can facilitate student learning, and positive
perceptions of instructors, which may engage students. Many researchers have
investigated the impact of use of humor in teaching in face-to-face environments (Aylor
& Opplinger, 2003; Bryant & Zillmann, 1988; Conkell, Imwold, & Ratliffe, 1999; Davies
& Apter, 1980; Downs, Javidi, & Nussbaum, 1988; Frymier & Wanzer, 1999; Frymier &
Weser, 2001; Gorham & Christophel, 1990; Kaplan & Pascoe, 1977; Sadowski &
Gulgoz, 1994; Wanzer, 2002; Wanzer & Frymier, 1999a, 1999b; White, 2001; Hauck &
Thomas, 1972; Ziv, 1988). Other research has investigated the impact of instructor use of
humor on enhanced quality of the student-instructor relationship (Welker, 1977) and on
affective learning (Wanzer & Frymier, 1999a).
Baumgartner and Morris (2008) showed humor-based teaching is clearly more
interesting for the students. Jaasma and Koper (1999) found that instructor use of humor
in teaching was superior as a predictor for formal and informal out of class
communication between instructors and students, and make these communication more
exciting for students. Milem and Berger (1997) found a positive relation between
students out of class communication with their instructors and their academic
integration. Instructor use of humor reduces physical and psychological distance with
students in the classroom (Andersen, 1979), which make humor communication excite
29


the student curiosity. Instructor immediacy resulting from the use of humor has been
positively associated with student engagement (Christensen, Curley, Marquez, & Menzel,
1995; Menzel & Carrell, 1999).
Recent research has distinguished between appropriate and inappropriate use of
humor (Wanzer, Frymier, Wojtaszczyk, & Smith, 2006). Wanzer et al. (2006) identified
four different categories of appropriate instructor use of humor (i.e., related humor,
unrelated humor, self-disparaging humor, and unplanned humor), similar to those
identified in prior research (Bryant et al., 1979; Downs et al., 1988; Gorham &
Christophel, 1990). Four other broad categories of inappropriate instructor humor were
identified and labeled as offensive humor, disparaging student humor, disparaging other
humor, and self-disparaging humor (Wanzer et al., 2006). Self-disparaging humor can be
used positively as appropriate humor (e.g. instructors telling life stories that may have
been embarrassing for them, or put them in an awkward situation), and negatively as
inappropriate humor (This type of humor involves a professor criticizing, poking fun of
or belittling himself/herself. e.g. professor says, I am such an idiot! to the students).
(See appendix A for details about Wanzers et al. classification of Instructors appropriate
and inappropriate humor)
Wanzer, Frymier, & Irwin (2010) suggested that the use of appropriate humor
related to course material enhances student learning in the classroom. They proposed the
Instructional Humor Processing Theory (IHPT) which offers an explanation for why
some types of instructor-generated humor result in increased student learning and others
do not. IHPT hypothesizes that humor related to instructional content correlates
positively with student learning, while inappropriate form does not. However, IHPT
30


investigated the impact of this humor on student learning in general. In this research we
extend IHPT by studying the impact of related and appropriate humor on engagement.
Building on IHPT, instructor use of appropriate humor (as defined by Wanzer et al.
(2006)) via a course-based social network is expected to have a positive impact on
student engagement in this course-based social network.
H3: Instructor use of humor via a course-based social network will have a positive
impact on the student engagement in the course-based social network.
3.3 Credibility and Engagement
Instructor credibility is defined as the extent to which an instructor is considered
to be believable, trusted by students, concerned about student welfare, and
knowledgeable about a given subject matter (McCroskey, 1998). Teven and Hanson
(2004) argue that an instructor who is perceived as a credible source is more likely to
relate well with students, and to improve their educational outcomes. Scholars have noted
that instructors who used behaviors to improve the clarity of the information presented to
the student, do engage students while presenting course content (Downs et al., 1988).
Methods viewed by students as a way to humanize instructors, make instructors appear
approachable, and create affect for both the course and the instructor, can be used by the
instructor to engage students (Nunziata, 2007).
Credibility has been consistently related to positive affect for both the subject
matter and instructors, and state motivation to learn (Frymier, 1994; Gorham, 1988).
Affect toward instruction is a state of psychological and emotional arousal toward the
instructor (Bloom, Englehart, Furst, Hill, and Krathwohl, 1956). Affect is positively
associated with students motivation and learning (Rodriguez, Plax, & Kearney, 1996).
31


Students who have higher levels of affect generally exhibit approach behaviors toward
the source of arousal and, as a result, are more engaged (Titsworth, 2001).
Short, Williams, and Christie (1976) presented social presence theory. Social
presence is described as the feeling that the group members communicate with people
instead of impersonal objects. Baker (2010, p. 5) argued when communication channels
are restricted, social presence decreases within a group. When social presence is low
within a group, group members often feel disconnected and cohesion levels are low.
When social presence is high, however, each group member has the feeling of joint
involvement. This suggest when instructor communicates with student via a course-
based social network, if instructors credibility is perceived as high by students,
instructors social presence will be perceived as high by students; and students will be
more engaged in this course-based social network.
In face-to-face environments, instructors must be seen to be perceived as present
(Picciano, 2002). In online environments, however, for the instructor to be perceived as
present requires actions. Examples of these actions include, developing consistent
patterns of interaction, communicating accessibility, providing consistent and substantive
feedback, moderating discussions effectively, and providing content expertise through
discussion posts to restart stalled discussions (Arbaugh and Hwang, 2006). These actions
represent instructor social presence in online communication, and expected to have a
direct impact on student engagement. These actions are more likely to be taken by the
instructor who is perceived as highly credible.
H4: Instructor Credibility will have a positive impact on student engagement in a
course-based social network.
32


3.4 Engagement and Educational Outcomes
Engaging systems have been described by users as: enticing users (Mayes, 1992);
drawing users into the activity (Laurel, 1991); and seducing and spurring users on
(Skelly, 1991). When asked by Jacques et al. (1995) what engagement meant to them,
users considered it to be a positive, interactive state, in which their attention was
willingly given and held. They described their feelings when interacting with engaging
software as curiosity, interest, confidence, and surprise. Users are engaged in a system
when it "holds their attention and they are attracted to it for intrinsic rewards" (Jacques et
al. 1995, p. 58). Engaged users enjoy the activity or product, which may make them want
to prolong the activity (Sandelands, 1988) or use the product again (Jordan, 1998).
Engagement is similar to flow, a state representing the extent of pleasure and
involvement in an activity (Csikszentmihalyi, 1975). In the organizational behavior
literature, employee engagement has been found to generate heightened morale,
cohesion, job satisfaction, organizational commitment, citizenship behaviors, customer
evaluations, reduced absenteeism, and consequently improved financial performance
(Harter, Schmidt, & Hayes, 2002; Saks, 2006; Salanova, Agut, & Peiro', 2005). In a
course-based social network, student engagement is a critical factor for student positive
development (Casalo, Flavia, & Guinali, 2007; Koh and Kim, 2003). In higher education,
student engagement has been defined as how involved or interested students appear to
be in their learning and how connected they are to their classes, their institutions, and
each other (Axelson & Flick, 2011, p. 38). Student engagement is a desired behavior
(Rocca, 2001), because it tends to improve student outcomes.
33


Engagement in a face-to-face classroom environment has been demonstrated to be
a positive indicator of educational outcomes. However, engagement in a course-based
social network, like a Facebook page for a course, is a more controversial issue.
Interaction between instructors and students online via Facebook has spurred debate
regarding its benefits and potential risks to students (Nixon, 2011). It has the potential to
provide for rich communication between students and instructors, but it is also a source
of other types of communication that may negatively affect educational outcomes.
Consequently, engagement in the Facebook IT artifact, rather than engagement in face-
to-face classroom environment, and its impact on the educational outcomes is an area that
needs more research. Specifically, this research examines two educational outcomes:
motivation to learn, and satisfaction with learning.
3.4.1 Impact of Engagement on Motivation to Learn. Past research indicates
that motivation to learn is a robust predictor of course outcomes and is influenced by both
individual and situational characteristics (Colquitt, LePine, & Noe, 2000; Noe, 1986;
Tannenbaum & Yukl, 1992; Noe & Schmitt, 1986; Quinones, 1995). Bothun (1998)
argues that the quality of learning depends on the student's level of motivation. Students
who perceived their instructor as communicating clearly and relevantly, and willing to
interact outside of class time reported greater motivation (Chesebro & McCroskey, 2001;
Jaasma and Koper, 1999). Course-based social networks provide students with the chance
to know more about their instructor. When students know more about their instructor,
they often express greater course motivation and view the classroom climate more
positively (Mazer, Murphy, & Simonds, 2007). Research by Gorham and Millette (1997)
suggests that student motivations can be sustained and diminished via classroom social
34


communication. Allen, Witt, & Wheeless (2006) suggest that competent instructors select
and employ more innovative types of communication to engage students with the
expectation that students will respond favorably.
Gorham (1988) and Dickmeyer (1993) found that instructor behaviors that engage
students, created a more immediate (enjoyable) classroom environment, which is
conductive to learning. Research also suggests that an immediate classroom environment
is likely to enhance student motivation to learn (Aylor & Opplinger, 2003; Downs et al.,
1988; Gorham & Christophel, 1992), this is because immediate classroom environments
engage students more in their classes. Parrott (1994) asserts that communication types
that engage students can be used as a teaching strategy; it can promote understanding and
increase attention and interest. Accordingly; it is hypothesized:
H5: Engagement in a course-based social network will have a positive impact on
the students motivation to learn.
3.4.2 Impact of Engagement on Satisfaction with Learning. Student
satisfaction refers to the degree to which students are satisfied about interactions with an
instructor (Frymier, 2005). Researchers have found that instructor behaviors, that engage
students, lead students to perceive instructors as clear (Wambach & Brothen, 1997) and
make them more satisfied with the course and the instructor. Goodboy (2009) found a
positive relationship between the instructor clarity and student satisfaction. Research by
Opplinger (2003) and Martin (2007) found that presenting educational materials in an
engaging manner, including using tools that students like and use in their everyday life,
arouses positive emotions that become associated with learning. This leads to more
positive attitudes towards education. Accordingly; it is hypothesized:
35


H6: Engagement in a course-based social network will have a positive impact on
the students satisfaction with learning.
3.5 Time Spent in the Online Social Network
Moores transactional distance theory (Moore, 1973; Moore and Kearsley, 1996)
provides an explanation for why the use of online communication tools may encourage
interactions among students and the instructor in an online environment. Moore (1973)
asserted that the physical separation in distance education leads to a potential
misunderstandings and communication gap between the instructor and the student;
however, increasing the time spent by student in the online social network decreases this
gap. The setting for Moores transactional distance theory is distance education; however,
it suggests that increasing the interaction time between instructor and students, by
utilizing advances in online communication tools, like Facebook, may bridge the distance
between students and the instructor in an online environment, which impacts the student
engagement.
In online settings, students are more likely and have more opportunity to spend
more time interacting with the classmates and the instructor than they do in a classroom.
Social network tools can be used to increase the level of interaction, thus allowing
students and instructors to reduce the psychological and physical distance between them
and to foster psychological closeness through interactions more than those offered by
face-to-face setting (Lemak, Shin, Reed and Montgomery, 2005).
Impact of instructor credibility on engagement is also expected to be moderated
by the amount of time spent in the online social network. Studies have revealed that
36


relational satisfaction increases as people spend more time on-line and the number of
messages helps to provide more information about one's relational partner (Wright,
2000, p 45). This suggests that the more time students spend in an online social network,
the more messages they are exposed to and thus, the more information they have about
their instructor. This will enhance the impact of credibility on engagement.
We hypothesized that the type of communication used by the instructor; self-
disclosure and use of humor, via a course-based social network, and instructor credibility
have an impact on engagement in course-based social networks. However, building on
Moores transactional distance theory, the level of engagement students will experience
will be influenced by the amount of time they typically spend interacting in the online
social network. Thus it is hypothesized that the impact of self-disclosure, humor, and
instructor credibility on student engagement in a course-based social network will be
moderated by the amount of time the student typically spends in that online social
network.
This suggests that students who spend more time interacting in the online social
network are more likely to be exposed to the instructor communication and engage with it
than students who spend less time in the online social network. Similarly, students are
more likely to engage with instructor that they perceive to be credible. But, the
underlying factors influencing credibility, believability, trustworthiness, concern about
student welfare, and subject matter knowledge (McCroskey, 1998), all can be influenced
by the students interaction with the instructor over time. Thus, a student who spends
more time interacting in the online social network is expected to be more engaged when
he/she perceives the instructor as more credible comparing to another student who spends
37


less time interacting in this online social network. In summary, the relationships between
the three independent variables (self-disclosure, humor and instructor credibility), and the
dependent variable (engagement) are expected to be stronger when the student spends
more time interacting in the online social network.
H7a: The impact of self-disclosure about topics related to the course on student
engagement in a course-based social network will be stronger when the student spends
more time in the online social network.
H7b: The impact of humor on student engagement in a course-based social
network will be stronger when the student spends more time in the online social network.
H7c: The impact of instructor credibility on student engagement in a course-based
social network will be stronger when the student spends more time in the online social
network.
3.6 Research Model
Figure III. 1 represents the research model being tested in the thesis. This study
investigates the impact of using self-disclosure and humor via a course-based social
network, and instructor credibility on student engagement in this social network, and
impact of this engagement on students perceived educational outcomes, student
motivation to learn and student satisfaction with learning.
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Figure III.l The Research Model.
To address the research questions posed by this thesis, two studies are conducted.
In the exploratory study (Figure IV.2), we conducted a survey to investigate the best
combination of communication types (among self-disclosure related interests, self-
disclosure unrelated interests and use of humor) that can be used by instructors via a
course-based social network to enhance student engagement in this social network. In the
main study (Figure V.l), we conduct a semester long experiment where an instructor
communicates with a class via a course-based social network (Facebook page or group).
The experiment compares a pair of classes with one class receiving Facebook
communication that includes instructor self-disclosure and humor along with course
related posts, and the other class (the control) receives only course related posts. The
difference in the outcomes between the two groups will then be measured utilizing a
39


survey and measurements of the actual engagement of the students within the course-
based social network.
40


CHAPTER IV
EXPORATORY STUDY
An exploratory study was conducted in which subjects were asked to read a
simulated Facebook page for a specific course, and respond to survey questions related to
their perceptions of the instructor self-disclosure and use of humor via this page. The
study explores the impact of self-disclosure, via a social network, related to work
experience, self-disclosure, via a social network, related to personal issues, and the
instructor use of humor, via a social network over and above face-to-face community.
One difference between this study and prior studies is the experimental treatment. Mazer,
Murphy, and Simonds (2007), for example, operationalized instructor self-disclosure, by
disclosing information about the instructor, like photographs and biographical
information, on a personal Facebook account and profile. In this exploratory study, the
instructor discloses about himself via a Facebook page or group specifically created for
the course. This type of self-disclosure is more likely to be perceived as being targeted at
the students in the course and, thus, is more likely to be accessed by students. In addition,
using Facebook pages and groups allow for posts specifically targeted at the course.
Social norms have clearly demonstrated that inappropriate humor, e.g. sexual jokes, is not
accepted in classroom, as such only appropriate types of humor (as defined by Wanzer et
al. (2006)) were used as a part of this study. Figure IV. 1 represents the research model
being tested in the exploratory study.
41


Figure IV.l The Research Model for the Exploratory Study
4.1 Participants
The participants are undergraduate students, enrolled in an introduction to IS
course at a Midwestern University. Students received extra credit for participating in the
study; however, students were asked but not required to participate in this study.
4.2 Manipulation
The independent variables in this study; self-disclosure about related work
experience, self-disclosure about personal issues, and use of humor via a social network,
are manipulated using posts in simulated Facebook pages for a university course, these
posts were posted by a fake instructor. We used a fake name for the instructor, so the
students are not affected by the real instructor credibility. The Facebook pages include
posts representing the different independent variables, along with other posts about
course related topics. The page was designed to be similar to a normal Facebook page
42


that might have been created for the simulated course. (See appendix B for a sample
Facebook page used by the study).
4.3 Procedure
There are eight different simulated Facebook pages for the same course, each with
a different combination of posts representing the independent variables. The participants
are randomly directed to one of these treatments, producing random assignments of the
participants to the treatment groups. The different Facebook page treatment options are
as follow:
Facebook page includes posts containing self-disclosure about related work
experience along with course related posts.
Facebook page includes posts containing self-disclosure about personal issues
along with course related posts.
1) Facebook page includes posts containing humor along with course related posts.
2) Facebook page includes posts containing self-disclosure about related work
experience and self-disclosure about personal issues, along with course related
posts (no humorous posts).
3) Facebook page includes posts containing self-disclosure about related work
experience, and humor, along with course related posts (no self-disclosure about
personal issues).
4) Facebook page includes posts containing self-disclosure about personal issues,
and humor, along with course related posts (no self-disclosure about related work
experience).
43


5) Facebook page includes posts containing self-disclosure about related work
experience, self-disclosure about personal issues, and humor, along with course
related posts (all of the options).
6) Facebook page includes course related posts only (control group).
The total number of posts on each Facebook page is 12 posts, similar to the number of
posts initially displayed on a normal Facebook page. At the beginning of the survey,
students were asked to read the posts in the simulated Facebook page, and to suppose
they are taking the mentioned course with the specific mentioned instructor. Then they
were asked to respond to survey questions that measure the outcome variables along with
manipulation check questions
4.4 Measurement
The survey instrument was drafted using the literature pertaining to the constructs.
The process included an exhaustive review of the related literature to derive the scales
items for the constructs. There are four constructs in the exploratory study. Rather than
developing new scales to measure these constructs, predefined and established measures
that have been validated and utilized in previous research is used in this study. The final
questions included in the survey are presented in Table IV. 1.
Table IV. 1: Measurement items used in the exploratory study
Construct Items Source
Self-
disclosure
related.
My instructor often posts her opinions about current Cayanus &
course related events Martin (2008)
My instructor often posts about her attitudes toward
course related events occurring on campus
My instructor often posts her opinion about course
related events in the community
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Table IV.l (cont.)
My instructor often shares her dislikes and likes related to the course content My instructor reveals relevant work experience in her posts
Self- disclosure unrelated. My instructor often posts her opinions about Cayanus & current course events unrelated to the course Martin (2008) My instructor often posts about her attitudes toward course unrelated events occurring on campus My instructor often posts her opinion about course unrelated events in the community My instructor often shares her dislikes and likes unrelated to the course content My instructor reveals personal information about herself in her posts
Use of Humor My instructor posts humor related to course Frymier, material Wanzer and My instructor posts funny props to illustrate a Wojtaszczyk concept or as an example (2008) My instructor posts jokes related to course content My instructor posts humorous story related to course content My instructor uses language in her posts in creative and funny ways to describe course material I found that the humor used by the instructor detract from the course experience The type and amount of humor used by this instructor encourages me to interact (comments/likes) on this Facebook page
Engagement This Facebook page kept me totally absorbed in Webster & Ho the browsing (1997) This Facebook page held my attention This Facebook page excited my curiosity This Facebook page was fun This Facebook page was intrinsically interesting This Facebook page was engaging
45


4.4.1 Self-Disclosure. Instructors Self-disclosure via a course-based social
network is measured using Cayanus and Martins (2008) Instructor Self-Disclosure Scale
(Appendix C). Items in the instructor disclosure scale are reworded to reflect instructor
self-disclosure about course related or unrelated issues. Example of the items, My
instructor often posts about her attitudes toward course related events occurring on
campus. Respondents indicate how well each item applies to their instructor using a
seven-point response continuum, ranging from completely disagree (1) to completely
agree (7). Negatively worded items were reverse coded in order to have higher scores
reflect greater perceived instructor disclosure. Cayanus and Martin (2008) reported an
alpha reliability of .80 for Instructors Self-disclosure measure. In the present study, the
Instructors Self-disclosure measure had a reliability of 0.90 and .95 for self-disclosure
about related issues and self-disclosure about unrelated issues respectively.
4.4.2 Humor. Instructor use of humor via a course-based social network
(appendix D) is measured by 7-items measure of instructor appropriate humor developed
by Frymier, Wanzer and Wojtaszczyk (2008), based on the appropriate and inappropriate
humor behaviors identified by Wanzer et al. (2006). The scale uses a 7-item Likert-type
response set ranging from 1 (completely disagree) to 7 (completely agree). Frymier,
Wanzer and Wojtaszczyk (2008) reported an alpha reliability of .85 for use of humor
measure. In the present study, the use of humor measure had a reliability of 0.93.
4.4.3 Engagement. Student engagement in the Facebook page is measured by 6-
items measure of engagement developed by Webster & Ho (1997) (appendix E). This
measure asks participants to report on how much they were engaged in the Facebook
course page, and it was used in this study because it measures engagement in IT artifacts
46


specifically, as this study is interested in engagement in the Facebook page for the course.
Responses were solicited using a 7-point Likert scale ranging from 1 (completely
disagree) to 7 (completely agree). Webster & Ho (1997) reported an alpha reliability of
.92 for engagement measure. In the present study, the engagement measure had a
reliability of 0.92.
4.5 Instrument Validation
A variety of validation checks were performed to assess the appropriateness of the
measures used. First, the data were checked for normality and outliers; the results of the
check suggested there were no problems regarding normality or outliers in this study.
Second, the scale items representing the constructs were assessed for content validity,
convergent validity, and discriminant validity.
Content validity represents the verification that the method of measurement
actually measures what it is expected to measure. Content validity is subjective and
judgmental but is often based on two standards: Does the instrument contain a
representative set of measures, and were sensible methods of scale construction used?
(Flynn, Sakakibara, and Schroeder, 1995).
In this study, the correspondence between the individual items and the concept
that these items are supposed to measure was considered. Several measures were taken to
check for content validity:
The survey instrument was reviewed by a number of reputable individuals to help
gauge content validity.
47


The questionnaire was pre-tested, using a pilot study, by gathering data from
thirty randomly selected participants. These participants were excluded from
further analysis.
Any inconsistencies or ambiguities were subsequently addressed.
Minor revisions were made to the questionnaire as a result of the pretests.
Convergent validity refers to a situation where items that should be related are in
reality related and correlate highly with one another. Correlations between items that
belong to the same construct were checked; results showed that convergent validity was
achieved.
Discriminant validity refers to a situation where the loading of each item on its
respective construct should be higher than its loading on the other constructs in the
model. This validity is tested by comparing the average inter-scale correlations to the
Cronbach alphas. Cronbach alphas should be greater than the average inter-scale
correlations to achieve acceptable discriminant validity (Karimi, Somers, and Gupta,
2001). This was the case for each of our measures in this study.
4.6 Common Method Bias Tests
Two primary ways were used to controlling for method biases; through (a) the
design of the studys procedures and (b) statistical controls.
In the design of the studys procedure, some of the techniques recommended by
Podsakoff, MacKenzie, Lee, and Podsakoff (2003), to controlling for method biases,
where used in this study. First, different response formats were used for the measurement
of the studys variable. This should reduce biases in the retrieval stage of the response
process by eliminating the saliency of any contextually provided retrieval cues. It should
48


also reduce the respondents ability and/or motivation to use previous answers to fill in
gaps in what is recalled and/or to infer missing details. Second, procedures were used at
the response editing or reporting stage. For example, respondents answers were allowed
to be anonymous. Another example was assuring respondents that there is no right or
wrong answers and that they should answer questions as honestly as possible. These
procedures should reduce peoples evaluation apprehension and make them less likely to
edit their responses to be more socially desirable, lenient, acquiescent, and consistent
with how they think the researcher wants them to respond (Podsakoff et al., 2003). Third,
the following recommendations of Podsakoff et al. (2003) were carefully considered, (a)
defining ambiguous or unfamiliar terms; (b) avoiding vague concepts and providing
examples when such concepts must be used; (c) keeping questions simple, specific, and
concise; (d) avoiding double-barreled questions; (e) decomposing questions relating to
more than one possibility into simpler, more focused questions; and (f) avoiding
complicated syntax.
It is possible that researchers using procedural remedies can minimize, if not
totally eliminate, the potential effects of common method variance on the findings of
their research. However, it is also useful to use statistical remedies that are available to
control for common method biases. In this research, two statistical tests were used to test
for common method bias. First, we perform Harmans single-factor test twice, once
including all independent variables and the dependent variable and the other time
including all independent variables. This method loads all items into an exploratory
factor analysis with no rotation and with number of factors fixed at 1, to see whether one
single factor does emerge or whether one general factor does account for a majority of
49


the covariance between the measures; if not, common method variances is not considered
as a pervasive issue. The first part of Table IV.2 shows results when the all of the
variables are included. The first factor explains only 29.385 % of the variance which is
not a majority (Greene and Organ, 1973). The econd part of Table IV.2 shows results
when the dependent variable is not included. The first factor explains only 32.946 % of
the variance which is not a majority (Greene and Organ, 1973). Accordingly, one cannot
conclude that common method variance is a concern.
Table IV.2 Part-1: Harmans single-factor test, Total Variance Explained by one single
factor when all of the variables are included.
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 5.877 29.385 29.385 5.877 29.385 29.385
Table IV.2 Part-2: Harmans single-factor test, Total Variance Explained by one single
factor when the dependent variable is not included.
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 4.942 32.946 32.946 4.942 32.946 32.946
Second, as recommended by Padsakoff et al. (2003), we included in the PLS
model a common method factor whose indicators included all the principal constructs
indicators to evaluate the size of common method variance. The procedure we have
followed is developed by Liang et al. (2007) and has been used by other authors (e.g.,
50


Fumeaux & Wade, 2011). We calculated each indicators variances substantively
explained by the principal construct and by the method. The results demonstrate that
indicator variance attributable to the common method factor range from 0.01% to 8%
with an average of 3.2% and a median 2.8%, whereas indicator variance attributable to
the underlying construct range from 54% to 81% with average of 70% and a median of
70%. The ratio of variance attributable to the underlying construct to that attributable to
the common method factor is about 22:1. Given the small magnitude of method variance,
we contend that the method is unlikely to be a serious concern for this study.
4.7 Exploratory Study Results
The experiment was conducted using 402 subjects. Subjects were randomly
assigned to one of the eight treatment groups. Demographics are presented in Table IV.3.
93.7% of the subjects were less than 35 years old, and 50.6% of the subjects were male.
Table IV.3 Sample Demographics
Variable Frequency Variable Frequency
Gender Having Facebook account
Male 50.6% Yes 90%
Female 49.4% No 10%.
Age Frequency of visiting
18-24 63.8% Facebook 31.3%
25-34 29.9% Not every day 13.4%
35-44 4.7% Once a day 15.4%
45-55 0.8% Twice a day 39.8%
55 and older 0.8% Three or more times a day
Level of education University level
Some College 52% Freshman 1%
Associates degree 21.3% Sophomore 15%
51


Table IV.3 (cont.)
Bachelors degree 21.7% Junior 47%
Masters degree 4.3% Senior 20%
Doctorate 0.8% Graduate student 18%
To evaluate the hypotheses model Partial Least Squares (PLS) Structural Equation
modeling (SEM) method was used. Item loadings, internal consistency, and discriminant
validity were used to evaluate the properties of the research model. The loading of each
indicator on its construct should have a path weight of at least 0.7 (Hulland, 1999). As
can be seen in Table IV.4, all items loading surpass this threshold.
Table IV.4 Loadings and cross-loadings.
Self-Disclosure Self-Disclosure
Engagement Humor Related Unrelated
Engl 0.81 0.34 0.17 -0.10
Eng_2 0.82 0.31 0.29 -0.17
Eng_3 0.89 0.39 0.29 -0.17
Eng 4 0.77 0.39 0.22 -0.07
Eng_5 0.88 0.42 0.28 -0.24
Huml 0.35 0.81 0.30 0.09
Hum 2 0.37 0.84 0.24 0.03
Hum 3 0.30 0.78 0.36 0.08
Hum 4 0.41 0.88 0.29 -0.05
Hum 5 0.40 0.83 0.38 0.06
SDll 0.22 0.32 0.74 -0.07
SD1_2 0.29 0.32 0.76 0.14
SD13 0.23 0.32 0.85 0.19
SD14 0.20 0.30 0.79 0.27
SD15 0.22 0.19 0.78 0.22
SD2_1 -0.18 0.02 0.14 0.90
SD22 -0.15 0.02 0.18 0.90
SD23 -0.13 0.12 0.22 0.88
52


Table IV.4 (cont.)
SD2_4 -0.16 0.02 0.22 0.88
SD25 -0.20 0.05 0.10 0.88
Each construct's composite reliability score was used to evaluate internal
consistency. The composite reliability scores (leftmost column of Table IV.5) all exceed
0.7 and thus are adequate for each construct (Hair et al, 1998). Discriminant validity
evaluating has two parts; firstly, the loading of each item on its respective construct
should be higher than its loading on the other constructs in the model, and secondly, the
Square Root of Average Variance Extracted (Square Root of AVE) for each construct
should be higher than the inter-construct correlations (Agarwal & Karahanna, 2000). In
table 4, by comparing the loading of each item on its respective construct to the other
cells in the same row, we can see that all items load higher on their respective construct
than the other constructs in the research model. Likewise, in Table IV.5, by comparing
the constructs Square Root of AVE on the diagonal to the inter-construct correlations on
the other cells, we can see that the Square Root of AVE for each construct is higher than
the inter-construct correlations without exception. These two comparisons suggest that
the model has good discriminant validity.
Table IV.5 Internal consistency and discriminant validity.
Composite Square Root of AVE and inter-construct correlations
Reliability Engagement Humor Self-disclosure Related Self-disclosure Unrelated
0.92 Engagement 0.84
0.92 Humor 0.44 0.83
0.89 Self-disclosure Related 0.30 0.38 0.79
0.95 Self-disclosure Unrelated -0.19 0.05 0.19 0.89
53


The results of the PLS SEM analysis are presented in Figure IV.3. Engagement
had an R-Square of .277. This means that 27.7% of the variance in engagement is
explained by self-disclosure related interests, self-disclosure unrelated interests, and use
of humor via a social network collectively (Agarwal & Karahanna, 2000). The path
coefficients between self-disclosure unrelated, humor and engagement were significant at
.01, while the path coefficients between self-disclosure related and engagement was
significant at .05. As summarized by Table IV.6, all three hypotheses were supported.
Table TV.6: Summary of hypotheses tests.
Hypothesis Supported
HI: Self-Disclosure Course Related 4 Engagement Yes
H2: Self-Disclosure Course Unrelated 4 Engagement Yes
H3: Humor ^ Engagement Yes
Figure IV.3 PLS SEM Results.
* Significant at .05 ** Significant at .01
54


CHAPTER V
MAIN STUDY
An experimental study was conducted in which subjects are engaged in a real
course-based social network, via Facebook page/group throughout a semester. In this
experiment instructors were provided with the chance to publish actual posts related to
the independent variables treatment resulted in the exploratory study, self-disclosure
about related interests and use of humor. The difference between independent variables
among the experimental groups (control and test groups) was measured.
Figure V.l shows the revised model conducted in the main study. Hypothesis 2
about the negative impact of self-disclosure via course-based social network about
unrelated interests on the student engagement was dropped, because the results from the
exploratory study indicate that there is a negative impact for self-disclosure via course-
based social network about unrelated interests on the student engagement. Consequently,
self-disclosure about unrelated interests may detract from learning thus will not be
extended to a real class environment. Hypothesis 4 about the impact of instructor
credibility on student engagement was added. Hypothesis 4 was not included in the
exploratory study, as students did not have a prior vision of credibility for the fictional
instructor so it does not fit before engagement in the exploratory study.
One addition to the research model in the main study is examining the impact of
engagement on educational outcomes. This addition improves contributions of this
research, because it examines the possible impact of the studys variables on the field
where this study applied.
55


Two traditional educational outcomes are examined in this study: student
motivation to learn, and student satisfaction with learning. Student motivation to learn
refers to student attempts to obtain academic knowledge or skills from classroom
activities by finding these activities meaningful (Brophy, 1987). Student satisfaction
refers to the degree to which students are satisfied about learning the course content and
about interactions with an instructor (Frymier, 2005).
These learning outcomes were chosen for three reasons. First, these variables are
traditionally studied as important outcomes in the classroom by instructional
communication scholars because they are representative of student achievement. Second,
instructor use of communication directly influences these outcomes (Kelley and Gorham,
1988; Richmond, Gorham, and McCroskey, 1987). Third, instructor confirmation (e.g.
instructor use of SNS in communication with students) is one positive instruction
behavior already associated with student motivation (Ellis, 2000, 2004) and may also be
associated with the additional learning outcomes of student satisfaction. Ellis (2000, p.
287) directly advised that future researchers should examine student satisfaction with
instructor confirmation behaviors.
The other addition in the revised model is adding three hypotheses about the
moderating impact of time spent in the online social network. These three moderating
hypotheses were not investigated in the exploratory study, as the students didnt engage
in a real course-based social network so we can investigate amount of time they spent
interacting online.
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Figure V.l: The Revised Research Model used in the Main Study
5.1 Sample
A sample of 266 undergraduate students enrolled in eight different courses taught
by three different instructors at the University of Colorado Denver are used for the main
study. Each one of the three instructors has similar number of participants among the
control and test groups in order to control for cross course variation. Students were asked
but not required to participate in the study and are assigned extra credit for participating
in the study. Four of these courses were assigned to the test group; four other courses
were assigned to the control group. The experiment utilized pairs of courses taught by the
same instructor as the experimental and control conditions to minimize variation in
outcomes caused by variation in teaching style or course content. A validation check will
be conducted to test if there is any significant difference in the independent variables
reported by the participants among the different classes in the control or experimental
57


groups. This will help determine if there are any systematic biases due to differences in
the instructors and classes. If the validation checks indicate the sample is not biased,
results should be generalizable to students population with similar characteristics. Table
V. 1 explains the courses used in this study.
Table V.l: University courses from which sample drawn from
Course Number of participants Instructor Control/Test Group
Introduction to IS 35 Instructor 1 Test Group
Introduction to IS 31 Instructor 1 Test Group
Introduction to IS 35 Instructor 1 Control Group
Introduction to IS 36 Instructor 1 Control Group
Introduction to IS 35 Instructor 2 Control Group
Introduction to IS 31 Instructor 2 Test Group
Introduction to IS 33 Instructor 3 Control Group
Introduction to IS 30 Instructor 3 Test Group
Total number of participants: 266 / Control group: group: 127 (52.3%) 139 (47.7%) / Test
5.2 Experiment Design
This study employed an experimental design to investigate the impact of
instructor self-disclosure about related interests, instructors use of humor via course-
based social network and instructor credibility on student engagement in this course-
based social network, and the impact of student engagement on student perceived
educational outcomes. In this experiment there are two groups of participants. In the first
58


group, the test group, instructors communicated with students by posting private
information, about his private experience, but related to the course content, and by
posting humorous posts, in addition to course related posts. In the second group, the
control group, instructors communicated with students by posting university and course
related posts only. Each experimental group participated in the experiment for an entire
semester. At the end of the experiment, participants in each group completed a survey
that measures the study outcomes. In addition, actual participant engagement within the
course-based social network (the Facebook page or group) was collected and recorded.
5.3 Procedure
The independent variables in this study, self-disclosure and use of humor, are
manipulated using Facebook posts representing these variables. In the experimental
group, participated instructors created Facebook page or group to communicate with the
students. Then they posted private information related to the course, in addition to
humorous posts. In both experimental and control groups instructors posted
announcements and materials related to the course. The Facebook pages or groups are
real ones. Students were asked to join this page/group and were encouraged to be
engaged in it by making comments, likes, and posts. At the end of semester, a survey was
conducted to test for the difference in the outcome variables, engagement and educational
outcomes. Actual engagement data was also recorded.
5.4 Measurement
Similar to the exploratory study, the survey instrument was drafted using
previously validated instruments. The same measures related to self-disclosure, humor
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and engagement, are used in the main experiment as were used in the exploratory study.
Table V.2 shows the measures used in the main study. This survey was conducted at the
end of the semester. Data about the actual engagement within the test and control groups
was also collected from the Facebook pages or groups for post-hoc analysis in this study.
This actual engagement data includes data about the number of comments, number of
likes, number of posts, and types of posts made by students.
Table V.2 Measurement items used in the main study
Construct
Items
Source
Self-Disclosure
related.
Use of Humor
My instructor often posts her opinions about current cayanus ,
course related events Martin (2008)
My instructor often posts about her attitudes toward
course related events occurring on campus
My instructor often posts her opinion about course
related events in the community
My instructor often shares her dislikes and likes related
to the course content
My instructor reveals relevant work experience in her
posts
My instructor posts humor related to course material
My instructor posts funny props to illustrate a concept
or as an example
My instructor posts jokes related to course content
My instructor posts humorous story related to course
content
My instructor uses language in her posts in creative and
funny ways to describe course material
I found that the humor used by the instructor detract
from the course experience
The type and amount of humor used by this instructor
Frymier,
Wanzer and
Wojtaszczyk
(2008)
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Table V.2 (cont.)
encourages me to interact (comments/likes) on this Facebook page
Credibility Intelligent/Unintelligent Expert/Inexpert Competent/Incompetent Informed/Uninformed Stupid/Bright Trained/Untrained Teven and McCroskey (1997)
Engagement This Facebook page kept me totally absorbed in the browsing This Facebook page held my attention This Facebook page excited my curiosity This Facebook page was fun This Facebook page was intrinsically interesting This Facebook page was engaging Webster & Ho (1997)
Motivation Motivated / Un-Motivated Interested / Uninterested Involved / Uninvolved Excited / Not Excited Looking forward to it / Dreading it Richmond (1990)
Satisfaction Dissatisfied / Satisfied Displeased / Pleased Content / Discontent Frymier & Houser (1998)
Following are more details about the measures added for the main study.
5.4.1 Creibility. Credibility is measured using Teven and McCroskeys (1997)
measure of instructor credibility (Appendix F). The instrument is composed of 5, seven-
step semantic-differential scales. It asks participants to evaluate their instructor.
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Responses were solicited using a 7-point bipolar adjective scale. Teven and McCroskeys
(1997) reported an alpha reliability of .95 for instructor credibility measure.
5.4.2 Motivation to Learn. Students motivation to learn is measured by 5-items
measure of students motivation to learn developed by Richmond (1990) (Appendix G).
It asks participants to report on their levels of state motivation toward a specific course
and instructor. Responses were solicited using a 7-point bipolar adjective scale.
Richmond (1990) reported an alpha reliability of .94 for motivation to learn measure.
Previous reliability coefficients ranging from .89 to .93 have been reported (Myers &
Zhong, 2004; Weber et al., 2005).
5.4.3 Satisfaction with Learning. Students satisfaction with learning is
measured by 3-items measure of students satisfaction with learning developed by
Frymier & Houser (1998) (Appendix H). It asks participants to report on their feelings of
satisfaction with their instructor and course. Responses were solicited using a 7-point
bipolar adjective scale. Previous reliability coefficients ranging from .92 to .95 have been
reported for the summed scale (Frymier, 2005; Frymier & Houser, 1998; Myers &
Bryant, 2002).
5.5 Main Study Results
The experiment was conducted using 266 subjects. Demographics are presented
in table V.3 97% of the subjects were less than 35 years old, and 54.6% of the subjects
were male.
To evaluate the hypotheses model Partial Least Squares (PLS) Structural Equation
modeling (SEM) method was used. Item loadings, internal consistency, and discriminant
validity were used to evaluate the properties of the research model. The loading of each
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indicator on its construct should have a path weight of at least 0.7 (Hulland, 1999). As
can be seen in Table V.4, all items loading surpass this threshold.
Table V.3 Sample Demographics
Variable Frequency Variable Frequency
Gender University level
Male (54.6%). Freshman (3.62%).
Female (45.4%). Sophomore (29.93%).
Junior (45.07%).
Senior (18.42%).
Graduate student (2.96)
Age Level of education
18-24 (70.7%). Some College (65.13%).
25-34 (26.3%). Associates degree (22.04%).
35-44 (2.6%). Bachelors degree (11.84%).
45- and older (0.4%). Masters degree or higher (.66%).
Table V.4 Loadings and cross-loadings.
Credibility Engagement Humor Motivation Satisfaction Self- Disclosure
Credl 0.88 0.44 0.46 0.42 0.32 0.36
Cred2 0.91 0.50 0.48 0.45 0.38 0.41
Cred3 0.88 0.48 0.45 0.41 0.27 0.41
Cred4 0.80 0.40 0.37 0.41 0.25 0.28
Cred5 0.84 0.43 0.38 0.37 0.23 0.38
Engl 0.41 0.77 0.41 0.40 0.40 0.45
Eng2 0.43 0.85 0.45 0.44 0.43 0.49
Eng3 0.47 0.90 0.48 0.44 0.47 0.49
Eng4 0.46 0.86 0.49 0.41 0.46 0.46
Eng5 0.45 0.88 0.45 0.40 0.43 0.39
Eng6 0.47 0.87 0.49 0.39 0.43 0.45
Huml 0.36 0.48 0.86 0.32 0.39 0.49
Hum2 0.41 0.47 0.88 0.35 0.37 0.43
Hum3 0.43 0.39 0.81 0.36 0.35 0.45
Hum4 0.47 0.50 0.89 0.43 0.40 0.45
Hum5 0.46 0.44 0.80 0.35 0.34 0.42
Motivl 0.44 0.42 0.37 0.84 0.35 0.27
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Table V.4 (cont.)
Motiv2 0.43 0.39 0.38 0.86 0.37 0.27
Motiv3 0.34 0.40 0.32 0.76 0.24 0.24
Motiv4 0.47 0.47 0.42 0.98 0.39 0.31
Motiv5 0.34 0.37 0.31 0.79 0.36 0.24
SDll 0.32 0.40 0.48 0.21 0.35 0.75
SD12 0.42 0.46 0.43 0.38 0.36 0.78
SD13 0.39 0.41 0.47 0.29 0.37 0.86
SD14 0.31 0.44 0.44 0.21 0.33 0.85
SD15 0.31 0.45 0.35 0.19 0.29 0.83
Satis 1 0.34 0.47 0.42 0.39 0.92 0.41
Satis2 0.31 0.47 0.41 0.37 0.93 0.38
Satis3 0.30 0.48 0.38 0.36 0.92 0.36
Construct's composite reliability scores were used to evaluate internal
consistency. The composite reliability scores (leftmost column of Table V.5) all exceed
0.7 and thus are adequate for each construct (Hair et al, 1998). Discriminant validity
evaluating has two parts; firstly, the loading of each item on its respective construct
should be higher than its loading on the other constructs in the model, and secondly, the
Square Root of Average Variance Extracted (Square Root of AVE) for each construct
should be higher than the inter-construct correlations (Agarwal & Karahanna, 2000). In
table V.4, by comparing the loading of each item on its respective construct to the other
cells in the same row, we can see that all items load higher on their respective construct
than on other constructs in the research model. Likewise, in Table V.5, by comparing the
constructs Square Root of AVE on the diagonal to the inter-construct correlations on the
other cells, we can see that the Square Root of AVE for each construct is higher than the
inter-construct correlations without exception. These two comparisons suggest that the
model has good discriminant validity.
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Table V.5. Internal consistency and discriminant validity.
Composite Reliability Square Root of AYE and inter-construct correlations
Credibility Engagement Elumor Motivation Self- Satisfaction Disclosure
.94 Credibility 0.74
.94 Engagement 0.47 0.74
.93 Humor 0.47 0.49 0.72
.89 Motivation 0.46 0.46 0.44 0.7
.95 Satisfaction 0.37 0.48 0.43 0.42 0.85
.91 Self-Disclosure 0.46 0.48 0.48 0.35 0.41 0.7
The results of the PLS SEM analysis are presented in Figure V.2. Engagement
had an R-Square of .427. This means that 42.7% of the variance in engagement is
explained by credibility, self-disclosure, and use of humor via a social network
collectively (Agarwal & Karahanna, 2000). Motivation had an R-Square of .233. This
means that 23.3% of the variance in motivation is explained by engagement. Satisfaction
had an R-Square of .259. This means that 25.9% of the variance in satisfaction is
explained by engagement. The path coefficients between credibility, self-disclosure, use
of humor and engagement were significant at .05, while the path coefficients between
engagement, motivation, and satisfaction were significant at .01. Results are summarized
by Table V.6, all five hypotheses were supported.
Table V.6 Summary of hypotheses tests.
Hypothesis Supported
HI: Self-Disclosure Course Related 4 Engagement Yes
H3: Humor 4 Engagement Yes
H4: Credibility 4 Engagement Yes
H5: Engagement ^Motivation Yes
H6: Engagement 4 Satisfaction Yes
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Figure V.2 PLS SEM Results.
* Significant at .05 ** Significant at .01
5.5.1 Moderating Impact of Time Spent in Online Social Networks. To check
for the moderating impact of time spent by students in the online social network, we used
multi-group moderation. The dataset was split into two parts based on the moderating
variable (time spent in the online social network), we checked the model two times, each
time with one of the two sub-samples, and then we checked whether there is significant
difference between the two results using a t-test.
The first sub-sample included participants who spend little time (low time) in the
online social network (less than the average time reported by students in the original
sample). The second sub-sample included participants who spent a lot of time (high time)
in the online social network (more than 60 minutes per day). We found significant
difference between the low and high sub-samples.
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Comparing the relationship between self-disclosure and engagement of students
that spent little time on the online social networks with those that spent more than 60
minutes per day at the online social network we see a significant difference between these
two groups. When students spent little time in the online social network; self-disclosure
had an insignificant impact on engagement, Figure V.3. While when students spend a
significant amount of time in the online social network; self-disclosure had a significant
impact on engagement, Figure V.4. A t-test was used to check if there is a significant
difference between these two results. It shows there is a significant difference between
the two results; P < 0.05. See Table V.7 Thus, hypothesis 7a is supported.
Comparing the relationship between humor and engagement of students that spent
little time on the online social networks with those that spent more than 60 minutes per
day at online social networks we see a significant difference between these two groups.
When students spent less time in the online social network; humor had an insignificant
impact on engagement, Figure V.3. While when students spent a lot of time in the online
social network; humor had a significant impact on engagement, Figure V.4. When we
used a t-test to check if there is a significant difference between these two results we
found that there is a significant difference between the two results. P < 0.05, table V.8.
Thus, hypothesis 7b is supported.
Comparing the relationship between credibility and engagement of students that
spent little time on the online social networks with those that spent more than 60 minutes
per day at online social networks we see a difference between these two groups, however,
this difference is insignificant. When students spent little time in the online social
network, credibility had a significant impact on their engagement, Figure V.3. However,
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when students were already spending a significant amount of time in the online social
network, credibility had an insignificant impact on engagement, Figure V.4. A t-test
revealed that there is not a significant difference between the two results. P = 0.19, Table
V.9. Thus, hypothesis 7c is not supported.
Figure V.3 PLS SEM Results for Low Time Spent Group.
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Figure V.4 PLS SEM Results for high time spent group.
Table V.7 T-Test Results for Self-Disclosure ^ Engagement
Groupl Low time spent Group2 High time spent
Sample Size 77 100
Regression Weight 0.096 0.349
t-statistic 2.406
p-value 0.017
Table V.8 T-Test Results for Humor ^ Engagement
Groupl Group2
Low time spent________High time spent
Sample Size 77 100
Regression Weight .11 .595
t-statistic 2.308
p-value 0.022
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Table V.9 T-Test Results for Credibility ^ Engagement
Groupl Low time spent Group2 High time spent
Sample Size 77 100
Regression Weight 0.435 0.56
t-statistic 1.315
p-value 0.190
5.5.2 Post-Hoc Analysis. Content analysis was used to classify the posts
published by the three participated instructor into the posts types presented in Table V.10.
Three different categories of posts were defined; self-disclosure about course related
interest posts, use of humor posts, and course related posts, as shown in Table V.10. All
112 posts were classified by one of the authors and a graduate student familiar with the
use of Facebook in education. The graduate student was provided with the classification
scheme, a description of each of the complaint categories, and an example of a post that
would correspond to each classification type. Some post examples were used as a training
sample to ensure that both coders agreed on the interpretation of the complaint types in
ensure that both coders agreed on the interpretation of the post types in Table V.10. Once
both coders agreed on the interpretation of the posts types, and both are comfortable
with the classification scheme, they independently classified the remaining 112 published
posts. When the classification was completed, a Kappa value of 0.94 (p < 0.000) was
computed and used as an index of inter-coder reliability (Cohen, 1960). Since this value
exceeded 0.80, the reliability of the coding was deemed acceptable (Grazioli &
Jarvenpaa, 2003). After the coders completed the independent coding, they met to discuss
each case where they disagreed and selected a mutually agreeable coding.
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Table V.10 The Classification Scheme for Posts Categories
Post Category Description of Post Category Example
Self-disclosure related interest When instructor posts private information, but related to the course interest. Like when instructor posts about work experiences related to course. One of the main tasks I was responsible for in my career was to keep track of all transactions that occur in the business. I used to use Excel to create a journal file; well discuss this in the next class
Use of humor When instructor posts appropriate humorous posts, like jokes and comic arts related to the course. Whoever is planning to not attend the next exam, please prepare yourself to be as smart as the book author for the makeup exam
Course posts related When instructor posts course related interests. Like when instructor posts announcement or further course explanations. Word 2010, like its predecessor, also has a Mini toolbar that will pop up when you select text for editing. This Mini toolbar is a quick and simple means for simple formatting and editing
After classifying all of the published posts, actual students engagement data,
represented by number of likes and comments made by them, will be assigned each
published post in a data point for the content analysis in the post hoc analysis, however
number of likes and comments is divided by the number of students who joined the
course-based social network to be standardized.
The post hoc analysis focuses on two different comparisons. First, a comparison
of the average engagement per course-based social network will be considered to
determine if students demonstrate more actual engagement in courses that include posts
that contain instructor personal information and humor than the courses that only posts
about course related topics. Second, within the courses a comparison of engagement
activity will be measured across the different types of posts to determine which type of
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post generates the most engagement. The post hoc analysis also provides additional
validation for the results found in the survey.
Table V.ll and Figure V.5 show a comparison of average students engagement
represented by number of likes and comments made by students per course-based social
network. Table V.12 and Figure V.6 show a comparison of average students engagement
represented by number of likes and comments made by students per experimental group
(test and control groups) by combining the courses that belong to the same experimental
group. Table V.13 and Figure V.7 show a comparison of average students engagement
represented by number of likes and comments made by students per post type (self-
disclosure, humor, and course posts).
Table V.ll Average engagement per a course-based social network
# of Likes # of Comments
Control 1 4% 1%
Control 2 2% 1%
Control 3 1% 0%
Control 4 1% 1%
Test 1 32% 9%
Test 2 16% 3%
Test 3 22% 2%
Table V.12 Average engagement per an experimental group
# of Likes # of Comments
Control 2% 1%
Test 23% 5%
Table V.13 Average engagement per post type
# of Likes # of Comments
Self-Disclosure 34% 6%
Humor 33% 8%
Course 6% 2%
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Figure V.5 Average Engagement per Course-Based Social Network
Figure V.6 Average Engagement per Experimental Group
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Figure V.7 Average Engagement per Post Type
5.5.3 Control Variables. We then checked for the following control variables:
Teaching style (online course vs. offline course); the instructor (instructor 1, 2, and 3);
time of experiment (spring 2012 vs. fall 2013); and gender (male vs. female).
To check for the impact of the control variable teaching style, we used multi-
group moderation. The dataset was split into two parts based on the control variable
(teaching style, online course and offline course); we checked the model two times, each
time with one of the two sub-samples. We then compared the results for students in the
offline courses, with those in online courses. In both groups all of the hypotheses were
supported. A t-test was used to check if there are any significant differences between
these two results. It shows there are no significant differences between the two groups.
Thus, there is no concern that the control variable teaching style has a significant
interaction effect on the model.
To check for the impact of the instructor on the results, we used multi-group
moderation. The dataset was split into three parts based on the instructor; we made three
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comparisons. We compared instructor 1 with instructors 2 and 3 combined; we compared
instructor 2 with instructors 1 and 3 combined; and we compared instructor 3 with 1 and
2 combined. In each one of these comparisons, we ran the model twice, each time with
one of the two sub-samples. In all of the groups all of the hypotheses were supported. A
t-test was used to check if there are any significant differences between these two results.
It shows there are no significant differences between the two groups. Thus, there is no
concern that the instructor has a significant interaction effect on the model.
To check for the impact of the control variable experiment time, we used multi-
group moderation. The dataset was split into two parts based on the control variable
(experiment time, spring 2012 and fall 2013); we checked the model two times, each time
with one of the two sub-samples. We then compared the results for students in the spring
2012, with those in fall 2013. In both groups all of the hypotheses were supported. A t-
test was used to check if there are any significant differences between these two results.
It shows there are no significant differences between the two groups. Thus, there is no
concern that the control variable experiment time has a significant interaction effect on
the model.
To check for the impact of the control variable gender, we used multi-group
moderation. The dataset was split into two parts based on the control variable (gender,
male and female); we checked the model two times, each time with one of the two sub-
samples. We then compared the results for male students, with female student. In both
groups all of the hypotheses were supported. A t-test was used to check if there are any
significant differences between these two results. It shows there are no significant
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differences between the two groups. Thus, there is no concern that the control variable
gender has a significant interaction effect on the model.
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CHAPTER VI
DISCUSSION
The results of this study indicate that the hypotheses model is supported. There is
a positive impact for instructor credibility, instructor self-disclosure, via a course-based
social network, about related topics, e.g. related work experience, and instructor use of
appropriate humor, via a course-based social network, on student engagement in this
course-based social network. There is a negative impact for instructor self-disclosure, via
a course-based social network, about unrelated topics, on student engagement in this
course-based social network. There is a positive impact resulting from student
engagement in a course-based social network on student motivation to learn, and on
student satisfaction with learning.
In addition, we found a moderating impact of time spent in the online social
network by student on the relationship between self-disclosure and engagement.
Meaning, as the student spends more time interacting in the online social network, the
impact of self-disclosure on engagement will be stronger. We also found a moderating
impact of time spent in the online social network by student on the relationship between
humor and engagement. Meaning, as the student spends more time interacting in the
online social network, the impact of humor on engagement will be stronger. However, we
didnt find a moderating impact of time spent in the online social network by student on
the relationship between credibility and engagement as hypothesized in hypothesis 7c.
The results of the two part study are summarized in Table VI. 1.
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Table VI.1 Summary of Hypotheses Tests.
Hypothesis Study Supported
HI: Self-Disclosure Course Related 4 Engagement Both Yes
H2: Self-Disclosure Course Unrelated 4 Engagement Exploratory Yes
H3: Humor 4 Engagement Both Yes
H4: Credibility 4 Engagement Main Yes
H5: Engagement 4 Motivation Main Yes
H6: Engagement 4 Satisfaction Main Yes
H7a: Time Spent 4 (Self-Disclosure -> Engagement) Main Yes
H7b: Time Spent 4 (Humor -> Engagement) Main Yes
H7c: Time Spent ^ (Credibility -> Engagement) Main No
This research confirms findings from prior studies, which found that when
instructors disclose private information about themselves, like photographs and
bibliographies; it positively affects educational outcomes. However; this study finds the
impact differs depending on the type of information that is disclosed. For example, when
the instructor posts about work experience related to course related concepts and content
it has a completely different effect than when the instructor posts about unrelated
personal issues, e.g. the instructors beliefs or life events and plans. This suggests the
later type of information distracts from the academic environment of the course.
This study also demonstrates that instructor use of humor via a course-based
social network group has a positive impact on student engagement in this course-based
social network group. This suggests that use of humor via a social network supports the
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instructor-student relationships and removes barriers between them. The results of this
study also help to clarify contradictory results about the impact of using humor in
educational environments. While some prior research has found that humor can be used
appropriately in the classroom to enhance learning and student perceived learning
outcomes (e.g. Gorham and Christophel, 1990), other research has demonstrated a
negative impact of humor on learning (e.g., Harris, 1989; Stuart & Rosenfeld, 1994;
Torok, McMorris, & Lin, 2004; Ziv, 1988). This study demonstrates that use of
appropriate humor (humor that conforms to the standards outlined in Wanzers
classification) does enhance engagement and hence perceived educational outcomes
when an instructor is communicating with students via a course-based social network.
These results also demonstrate that the time a student spends in the online social
network moderates the impact of communication types used by the instructor (instructor
self-disclosure and use of humor) on the student engagement. When spending more time
interacting in the online social network, the student will be more exposed to the instructor
posts related to self-disclosure and humor; consequently, this students engagement will
be more impacted by these posts, compared to another student who spends less time in
the online social network.
Instructor credibility also has a positive impact on engagement; credibility brings
more reason for students to get engage to begin with. However, after spending more time
in the online social network the student could figure out that the instructor is less or more
credible. Accordingly, the impact of credibility on engagement could be stronger or
weaker depending on the amount of time spent, and we found it to be weaker; however
not significantly weaker. We found that the impact of credibility on engagement is
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always significant whether spending low or high amount of time in the online social
network.
6.1 Theoretical Contribution and Implications to Research
This paper contributes to IS research by deepening our understanding of student
engagement in course-based social networks by conceptualizing an engagement model
that supports use of online social network systems for engaging students. The proposed
model demonstrates that engagement is an important factor when studying more
interactive communication-based systems like online social network systems. Moreover,
this research provides new determinants that impact student engagement in a course-
based social network, self-disclosure and use of humor. These factors can be utilized by
IS researchers to study the antecedents and consequences of different types of
communication content and understand the relationships between these communication
types used via a course-based social network and engagement in this course-based social
network.
Results of this study build on the communication privacy management theory
(Petronio, 2002) to expand our understanding of types of private information that might
be the most beneficial and pose the fewest risks in professional settings like college
classrooms. Communication privacy management theory stated that there is tension
between the decision to disclose or to conceal private information; because self-
disclosure has both benefits and risks. However, the theory didnt explain what factors
make self-disclosure associated with benefits or with risks. This research extended
communication privacy management theory by examining the impact of self-disclosure
type and relevance on the risks and benefits of that disclosure. We defined two types of
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self-disclosure based on its relevance to the course-based social network; self-disclosure
related to the course topics, and self-disclosure unrelated to the course topics. We found
that the related self-disclosure is associated with benefits of the this self-disclosure and
has a positive impact on engagement, while the unrelated self-disclosure is associated
with risks of the this self-disclosure and has a negative impact on engagement. This
would help to understand how to maximize the benefits of self-disclosure, minimize the
risks of self-disclosure, and to take the decision the decision to disclose or to conceal
private information.
The research also contributes to the instructional humor processing theory. It
expands our understanding of the instructor use of humor, via a course-based social
network, and its impact on the student engagement. The theory stated that the instructor
use of appropriate humor, related to the course material, correlates positively with student
learning. This research expanded the instructional humor processing theory, by
examining the impact of the instructor use of appropriate humor, related to course
material, on the student engagement specifically. This research also expanded the IS
theory by utilizing Wanzers classification of appropriate and inappropriate instructor use
of humor. This assist in confirming that instructor use of humor that we used in this
research is the appropriate type of humor.
The research also contributes to theory by providing an engagement model that is
unique to online educational setting, by utilizing Moores transactional distance theory, to
study the moderation impact of time spent by student in the online social network.
Moores transactional distance theory asserts that the physical separation in distance
education leads to a potential misunderstandings and communication gap between the
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instructor and the student. However, the theory stated that increasing the time spent by
student in the online social network decreases this gap. Our research expanded Moores
transactional distance theory, by finding that the instructor-students communication, via a
course-based social network, provides the student with the opportunity to spend more
time to interact in the online social network; because in online setting, students are more
likely and have more opportunity to spend more time interacting with the classmates and
the instructor than they do in a classroom. That reduces the psychological and physical
distance between them and foster psychological closeness through interactions more than
those offered by face-to-face setting. This also bridges the distance between students and
the instructor, increasing student engagement. Accordingly, we found that increasing the
time spent by students in the online social network moderates the impact of instructor use
of different communication types (instructor self-disclosure and use of humor) on
engagement. The student perception of the instructor use of these communication types
and its impact on engagement varied just because of the amount time the student spends
online.
6.2 Implications to Practice
Faculty members in higher education institutions can use the results of this
research to improve student engagement, and hence, improve students perceived
educational outcomes. This study provided guidance about what content is appropriate to
be posted in social networks, like Facebook, and what content is not appropriate. In a
changing world where the line between social and professional communication is
increasingly blurred, this guidance is essential. For example, some instructors have been
known to "friend" their students via personal social network sites like Facebook
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(Rutledge, 2011). Results of this study suggest that this practice might not be appropriate
if the instructor also uses the Facebook profile page to post personal information to
family and friends. This research does not suggest that instructors should avoid social
network sites like Facebook. Instead, it suggests that instructors should create pages or
groups targeted to their students and use those pages or groups to send a clear message to
students that they care about them, and they are interested in fostering a positive
relationship with them.
When instructors interact with students via Facebook, students have the
opportunity to use technologies they already use in everyday life, in the classroom. This
provides them with new and more accessible resources to enhance their class knowledge,
improve their relationships with their instructors, and positively impact their perceived
educational outcomes, like their motivation to learn and their satisfaction.
These research results, about the importance of social contact between instructor
and students outside of the classroom (e.g. in Facebook groups), has also implications for
designers of learning management systems. Designers of learning management systems
should try to facilitate posting of content on social sharing platforms beside the learning
management systems. Other solution could be by supporting the learning management
systems ability to include content directly from those social network sites inside the
learning management systems itself. This will improve the ability of these learning
management systems to improve student engagement and student educational outcomes.
6.3 Limitations and Future Research
Three types of online communication have been studied as a part of this research:
self-disclosure about related work experience, self-disclosure about unrelated personal
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issues and the use of humor. However; only two levels of each factor have been used,
treatment or no treatment (e.g., use humor vs. no humor). There may be an optimum
amount of each type of message to use when communicating in online social networks,
for example, a little humor may improve outcomes, but too much may have negative
consequences. Future research could replicate this study with more groups and additional
factor levels to help capture the impact of different amounts of these factors on student
outcomes.
The Facebook simulated pages treatment used in the exploratory study may not
adequately represent the independent variables effect that a longer and deeper experiment
can provide. In the exploratory study, engagement in a course-based social network is
measured by asking the participants about their expected engagement. However, our
longer experiment provided the opportunity for recording and measuring the actual
engagement in the course-based social network by noticing the student interaction rate on
an actual course-based social network. This guaranteed a higher level of internal validity
where the impact on the outcomes measures comes only from the treatment factors.
This research can also be extended by investigating appropriate types of
communication outside the higher-education domain. Today, 90% of firms are using
social networks as a part of their online marketing efforts (Stelzner, 2011). Future
research can help improve their understanding of how to engage their customers around
their products, services and brands, and increase customer loyalty. Future research can
build on results of this study to increase the confidence of firms that are not already using
online social networks for engaging customers. Moreover, providing a future model that
can be used to understand customer engagement in online social networks can facilitate
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deep and enduring affective bonds between customers and suppliers in the firm-hosted
social networks. This can supports a variety of organizational activities beyond marketing
like customer relationship management (CRM), the innovation process for products, and
the recruitment of talent for these firms.
Finally, communication in virtual teams is another area where this research can be
applied. Virtual team leaders typically have less direct control over team members that
are working from different locations. Future research can help the virtual team leader to
engage members of the team more in their tasks, and motivate them to be more
cooperative. This should allow members to have better relationships with each other,
which can increase collaboration and improve outcomes for the team.
6.4 Conclusions
Online social networks are increasingly being used in different fields. In higher
education, students and faculty members have begun to realize the benefits that can be
achieved when adopting online social networks like Facebook in the classroom.
However, little is known about the types of communication that can best be used via an
online social network to enhance engagement among members of this online social
network.
This research enhances our knowledge about the use of Facebook in classrooms,
by investigating how instructors can use such a technology to engage the students, and
advance their perceived educational outcomes. It demonstrates that it is not sufficient to
simply communicate with students. How you communicate and what you disclose also
has a tremendous impact on student outcomes.
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The thesis included two studies. The first study involves an exploratory study that
utilizes a survey to investigate the best combination of communication types (among self-
disclosure related interests, self-disclosure unrelated interests and use of humor) that can
be used by instructors via a course-based social network to engage students in this
network. The second study involves a real-world experiment. In this experiment study,
we started with the results from the exploratory study about the best combination of
communication types that can be used to engage students, we added the instructor
credibility that can affect the student engagement, the educational outcomes that can be
affected by the student engagement, and the time spent by the student in the online social
network that moderates the research hypotheses, to the research model in the experiment
study. Then we conducted an experiment, where an instructor communicates with
students via a real course-based social network for an entire semester.
The thesis investigated the communication types used by the instructor via a
course-based social network, because we found that the type of communication has a
direct impact on relationships building and development. Building on the literature and
the use of course-based social networks in practice, we investigated self-disclosure, both
related and unrelated to the course, and use of humor, as types of communication that can
be used by instructor when communicating with students via a course based social
network. We found that, self-disclosure that is related to the course content, and use of
humor, are positively impact the student engagement in a course-based social network.
Self-disclosure that is unrelated to the course content, however, found to have a negative
impact on student engagement. The research also investigated the instructor credibility,
and its perception in an online setting. Building on social presence theory, we
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hypothesized that instructor credibility has a positive impact on student engagement, and
this was supported by this research.
The thesis investigated student engagement in a course-based social network as
the central theme in research surrounding social network use. It also investigated the
impact of this engagement on student educational outcomes, student motivation to learn,
and student satisfaction with learning. These outcomes are traditionally studied as
important outcomes in the classroom by instructional communication scholars because
they are representative of student achievement.
The thesis study found that studying engagement in online settings specifically is
essential. Relying on the current research about engagement in face-to-face setting is not
sufficient when investigating engagement in online social networks. The main difference
between the two settings is the amount of time that the student can spend interacting in
the online social network. Accordingly, we investigated the moderating impact of time
spent by student in the online social network. In this thesis, time spent by student in the
online social network was found to significantly moderate the impact of communication
types used by the instructor in a course-based social network on student engagement in
this network. The impact of instructor self-disclosure on student engagement found to be
significantly stronger for students who spent more time in the online social network
comparing to those who spent less time. Similarly, the impact of instructor use of humor
on student engagement found to be significantly stronger for students who spent more
time in the online social network comparing to those who spent less time.
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Full Text

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IMPROVING STUDENT ENGAGEMENT USING COURSE-BASED SOC IAL NETWORKS By Jehad Mohammad Imlawi B.A. Mutah University, 2003 M.A. Amman Arab University, 2006 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Computer Science and Information Systems 2013

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ii This thesis for the Doctor of Philosophy degree by Jehad Mohammad Imlawi has been approved for the Computer Science and Information Systems Degree by Judy Scott, Chair Dawn Gregg, Advisor Jahangir Karimi Min-Hyung Choi 4/1/2013

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iii Imlawi, Jehad, Mohammad. (Ph.D, Computer Science a nd Information Systems) Improving Student Engagement Using Course-Based Soc ial Networks Thesis directed by Associate Professor Dawn Gregg ABSTRACT This study proposes an engagement model that suppor ts use of course-based online social networks for engaging student, and hence, improving their educational outcomes. This research demonstrates that instructors who create c ourse-based online social networks to communicate with students can increase the student engagement in these online social networks, and increase student perceived educationa l outcomes. The model is developed and tested in a higher education setting. The primary contribution of this research is deepen ing insights into the information systems and communication artifact by conceptualizi ng a model that helps researchers understand the reasons why some communication types used by instructors via a coursebased social network, such as appropriate humor mes sages, can improve engagement among students, and improve their perceived educati onal outcomes, while other communication types may negatively affect engagemen t within this course-based social network One other contribution is studying the mo derating impact of time spent by student in the online social network, as this facto r makes the studying of engagement in online setting is unique than engagement in face-to -face setting. The form and content of this abstract are approved. I recommend its publication. Approved: Dawn G. Gregg

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iv DEDICATION I lovingly dedicate this thesis to my beautiful wif e, Rasha, who has been with me through every step of this thesis, even when I coul dn't be with her. This work is also dedicated to my beloved kids, Dim a, Sara, and Ahmad, "... to the moon and back." Finally, this work is dedicated to my parents, Moha mmad and Kadijah, who taught me the most important things I will ever lea rn: love of family, integrity, hard work, persistence.

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v ACKNOWLEDGMENTS Though my name appears on the cover of this thesis, a great many people have contributed to its production. I owe my gratitude t o all those people who have made this thesis possible and because of whom my graduate exp erience has been one that I will cherish forever. Foremost, I would like to express my sincere gratit ude to my advisor Professor Dawn Gregg for the continuous support of my Ph.D st udy and research, for her patience, motivation, enthusiasm, and immense knowledge. Her guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my Ph.D study. She believed I would accomplish this goal, even when I didn't. I would also like to thank the other members of my committee: Jahangir Karimi, Judy Scott, and Min-Hyung Choi for the time and har d work they have invested in my education. I would like to acknowledge all the professors and instructors in University of Colorado Denver who participated in the experiment in this thesis or allowed me to use time of their classes to collect data for this thes is. Finally, I would like to acknowledge all the fellow PhD students in the Information Systems, Business School for their valu able feedback and help in our meetings.

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vi TABLE OF CONTENTS CHAPTER I. INTRODUCTION ................................... ................................................... ..................... 1 1.1 Research Problem and Scope .................... ................................................... ..... 1 1.2 Topic Importance .............................. ................................................... ............. 3 1.3 Research Questions ............................ ................................................... ............ 4 1.4 Research Approach ............................. ................................................... ........... 5 1.5 Research Contribution ......................... ................................................... .......... 5 1.6 The Thesis Outline ............................ ................................................... ............. 6 II. LITERATURE REVIEW ............................. ................................................... ............... 7 2.1 Engagement..................................... ................................................... ............... 8 2.2 Relationship Building Communication............ ............................................... 12 2.2.1 Self-Disclosure. ............................ ................................................... ....... 12 2.2.2 Humor. ...................................... ................................................... .......... 14 2.3 Credibility ................................... ................................................... ................. 16 2.4 Facebook in Education ......................... ................................................... ........ 20 III. THEORETICAL BACKGROUND ....................... ................................................... .. 24 3.1 Self-Disclosure and Engagement ................ ................................................... 24 3.2 Humor and Engagement .......................... ................................................... .... 29 3.3 Credibility and Engagement .................... ................................................... .... 31 3.4 Engagement and Educational Outcomes ........... ............................................. 33 3.4.1 Impact of Engagement on Motivation to Learn. .................................... 34 3.4.2 Impact of Engagement on Satisfaction with Lea rning. .......................... 35 3.5 Time Spent in the Online Social Network ....... ............................................... 36

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vii 3.6 Research Model ................................ ................................................... ........... 38 IV. EXPORATORY STUDY .............................. ................................................... .......... 41 4.1 Participants .................................. ................................................... ................. 42 4.2 Manipulation .................................. ................................................... .............. 42 4.3 Procedure ..................................... ................................................... ................ 43 4.4 Measurement ................................... ................................................... ............. 44 4.4.1 Self-Disclosure. ............................ ................................................... ....... 46 4.4.2 Humor. ...................................... ................................................... .......... 46 4.4.3 Engagement................................... ................................................... ...... 46 4.5 Instrument Validation ......................... ................................................... ......... 47 4.6 Common Method Bias Tests ...................... ................................................... .. 48 4.7 Exploratory Study Results ..................... ................................................... ...... 51 V. MAIN STUDY ..................................... ................................................... ..................... 55 5.1 Sample......................................... ................................................... ................. 57 5.2 Experiment Design.............................. ................................................... ......... 58 5.3 Procedure ..................................... ................................................... ................ 59 5.4 Measurement ................................... ................................................... ............. 59 5.4.1 Creibility. ................................. ................................................... ........... 61 5.4.2 Motivation to Learn. ........................ ................................................... ... 62 5.4.3 Satisfaction with Learning. ................. ................................................... 62 5.5 Main Study Results ............................ ................................................... .......... 62 5.5.1 Moderating Impact of Time Spent in Online Soc ial Networks. ............ 66 5.5.2 Post-Hoc Analysis. .......................... ................................................... .... 70 5.5.3 Control Variables. .......................... ................................................... ..... 74

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viii VI. DISCUSSION .................................... ................................................... ...................... 77 6.1 Theoretical Contribution and Implications to Re search ................................. 80 6.2 Implications to Practice....................... ................................................... ......... 82 6.3 Limitations and Future Research ............... ................................................... .. 83 6.4 Conclusions ................................... ................................................... ............... 85 REFERENCES ........................................ ................................................... ...................... 88 APPENDIX .......................................... ................................................... ........................ 107 A. Appropriate and Inappropriate Humor ............ ................................................... ........ 107 B. Examples of Facebook Pages used in the Study ... ................................................... .. 117 C. Instructor Self-Disclosure Scale ............... ................................................... ............... 121 D. Instructor Use of Humor Scale................... ................................................... ............. 123 E. Student Engagement Scale ....................... ................................................... ............... 124 F. Instructor Credibility Scale ................... ................................................... ................... 125 G. Student Motivation to Learn Scale............... ................................................... ........... 126 H. Student Satisfaction with Learning Scale ....... ................................................... ........ 127

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ix LIST OF TABLES Table IV.1 Measurement items used in the exploratory stud yÂ…Â…Â…Â…Â…Â…Â…Â…Â…... 44 IV.2 Common Method Bias TestsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â… 50 IV.3 Sample Demographics in Exploratory StudyÂ…Â…Â…Â…Â…Â… Â…Â…Â…Â…Â…... 51 IV.4 Loadings and cross-loadingsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…......... ....................... 52 IV.5 Internal consistency and discriminant validity Â…Â…Â…................................. 53 IV.6 Summary of hypotheses testsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…......... ...................... 54 V.1 University courses from which sample drawn from Â…................................. 58 V.2 Measurement items used in the main studyÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â… Â….. 60 V.3 Sample DemographicsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â….. 63 V.4 Loadings and cross-loadingsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â… 63 V.5 Internal consistency and discriminant validityÂ…Â…Â…Â…Â… Â…Â…Â…Â…Â…Â…. 65 V.6 Summary of hypotheses testsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â… 65 V.7 T-Test Results for Self-Disclosure EngagementÂ…Â…Â…Â…Â…Â…Â…Â…Â…. 69 V.8 T-Test Results for Humor EngagementÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…... 69 V.9 T-Test Results for Credibility EngagementÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…. 70 V.10 The Classification Scheme for Posts CategoriesÂ…Â…Â…Â…Â… Â…Â…Â…Â…Â….. 71 V.11 Average engagement per a course-based social networ kÂ…Â…Â…Â…Â…Â…Â…. 72 V.12 Average engagement per an experimental groupÂ…Â…Â…Â…Â…Â…Â… Â…Â…Â…. 72 V.13 Average engagement per post typeÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â… 72 VI.1 Summary of Hypotheses TestsÂ…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…Â…. 78

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x LIST OF FIGURES Figure III.1 The Research Model……………………………………………………….. 39 IV.1 The Research Model for the Exploratory Study…… …………………….... 42 IV.3 PLS SEM Results………………………………………………………….. 54 V.1 The Revised Research Model used in the Main Stu dy…………………….. 57 V.2 PLS SEM Results – Main Study………………………………………….... 66 V.3 PLS SEM Results for Low “Time Spent” Group……………………… ….. 68 V.4 PLS SEM Results for high “time spent” group…………………… ………. 69 V.5 Average Engagement per Course-Based Social Network… ………………. 73 V.6 Average Engagement per Experimental Group………………………… …. 73 V.7 Average Engagement per Post Type………………………………………. 74 B.1 A sample simulated Facebook page used in the explor atory study …… …. 117 B.2 A screenshot of a real Facebook page used in the ma in study……………. 120

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1 CHAPTER I INTRODUCTION IS researchers have become increasingly interested in understanding how to organize and facilitate the development of online s ocial network groups (Nambisan and Nambisan, 2008; Wellman and Gulia, 1999, Bagozzi an d Dholakia, 2002, 2006, Lin and Lee, 2006), which is defined by Cothrel (2000) as i ndividuals interacting virtually via computer-mediated communications (CMC). Many reason s underlie this interest, including the ability of a sponsor or administrator in these groups, such as instructors, to facilitate deep and enduring affective bonds with m embers in these groups (Hagel and Armstrong, 1997, Dou and Krishnamurthy, 2007). Social network sites (such as Facebook, MySpace, an d Twitter, etc.) provide the opportunity for building and maintaining online soc ial network groups around a specific interest (such as an educational interest). For exa mple, instructors in higher education can create a course-based social network to engage stud ents. Some of the promise and popularity of online social networks lie in their a bility to offer an alternative means to communicate, and collaborate (Jarvenpaa et al., 200 7). As such, they carry the potential to dramatically change the ways in which we interac t with one another in both real and online world (Chaturvedi, Dolk, and Drnevich, 2011) 1.1 Research Problem and Scope Unlike prior information technologies, the central theme in research surrounding social network use is engagement (Hassenzahl & Trac tinsky, 2006), as opposed to system

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2 usablity, as defined under TAM (Davis, Bagozzi, and Warshaw, 1989). Research suggests that complex computer mediated communicati on systems must be not only usable, but engaging (OÂ’Brien and Toms, 2003). This suggests that to understand the adoption and use of online social networks requires an examination of engagement by users within these networks (OÂ’Brien and Toms, 2003 ). This represents a fundamental shift in focus from the design of the technology it self to how the technology is used by participants within these online social networks. This research utilizes Communication Privacy Manage ment theory (CPM) (Petronio, 2002) and Instructional Humor Processing theory (IHPT) to improve our understanding of how instructor use of self-disclos ure and humor within a course-based social network can improve student engagement. This research also utilizes social presence theory to investigate the impact of instru ctor credibility on student engagement. Then it investigates the impact of engagement on pe rceived educational outcomes. These outcomes include student motivation to learn and sa tisfaction with learning. The moderating impact of time spent in the online socia l network by the student is investigated by this study as well. While higher ed ucation is not a traditional business environment, students in higher education are typic ally the early adopters for the newest technologies available and using higher education f or this study may provide insights into how social networks can transform interaction in ot her domains as well (as called for by Benbasat & Zmud, 2003; Agarwal & Lucas, 2005; DeSan ctis, 2003, and King & Lyytinen, 2004).

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3 1.2 Topic Importance The role social networks play in higher education i s gaining increased attention with the rise of massively open online courses (MOOCs). In f act, many higher education leaders see MOOCs as the “future of higher education” (Scha ffhauser, 2012). These systems rely on online social networks to create connectedness a nd to improve engagement. MOOCs use social networks to create and sustain the socia l dimension of learning, and to enhance knowledge production rather than simply providing a platform for knowledge consumption (Bousquet, 2102). Yet very little is kn own about the types of messages that are appropriate to be shared between instructors an d students in these communities. Most research on instructor student interaction con ducted to date has been in face-to-face environments; little previous research has studied their impacts in online environments, like Facebook. Thus, there is a need to better unde rstand how communication between instructor and students can be enhanced through the use of social network tools. It is possible that what is true in face-to-face en vironments may not be true in online environments. First, in online environments, students are more likely and have more opportunity to spend more time interacting wit h other students and the instructor, which is not necessarily true in face-to-face envir onments. This raises the contextual condition that says why the studied relationships i n this research could be stronger or weaker in online environments, and may not be the s ame as in face-to-face environments. Second, online environments have fewer cues about h ow to interpret messages. For example, the use of humor online could potentially pose problems because people often rely on environmental cues when deciding how to int erpret a humorous message (Leventhal & Cupchik, 1976). Third, Child & Petroni o (2010) have found that

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4 individuals have different perceptions and rules fo r face-to-face self-disclosure than they have for self-disclosure in online social networks. For example, their research suggests that individuals tend to reveal more private inform ation online (Child & Petronio, 2010). Fourth, Research also suggests that students may pe rceive an instructor use of Facebook as an attempt to foster positive relationships with students, and perceive the instructor as more available to students, which may improve perce ptions of instructor credibility (Mazer, Murphy, and Simonds, 2007; OÂ’Sullivan, Hunt and Lippert, 2004), and hence improve student engagement. Accordingly, student ex pectations in online environments are likely to be different than in face-to-face env ironments. The discrepancy between engagement online and offline highlights the need f or research on engagement in online social networks. This thesis addresses the critical need for an improved understanding of what messages are most effective in social networks used to support education. 1.3 Research Questions This thesis is motivated by the following questions : What types of communication messages can be used by instructors, via a coursebased social network, to improve student engagement within this social network? How does instructor credibility impact student enga gement in a course-based social network? How does engagement, in a course-based social netwo rk, impact educational outcomes, like motivation to learn and student sati sfaction with learning? Does the amount of time the student spends in the o nline social network moderate the impact of instructor credibility, instructor se lf-disclosure, and instructor use of humor on engagement?

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5 1.4 Research Approach This research utilizes Facebook pages and groups to provide instructors with the opportunity to communicate directly with students o n the most popular social network platform without requiring the instructor to friend students, or to have access to their private profiles. The thesis includes two studies. The first study involves an exploratory study that utilizes a survey to investigate the bes t combination of communication types that can be used by instructors via a course-based social network (Facebook page or group). The second study involves a real-world expe riment, where an instructor communicates with students via a course-based socia l network group for an entire semester. The experiment includes two groups. In th e test group the instructor communicates using the most effective communication types identified in the exploratory study. In the control group the instructor only pos ts messages related to course content and school announcements via the Facebook page or g roup. The difference in the outcomes between the two groups is then measured us ing a survey and by recording the actual engagement. The unit of analysis is at the i ndividual level as perceptions of student engagement and individual education outcomes are co nsidered. Structural equation analysis is used to examine the proposed hypotheses and test for significant differences between groups. A post-hoc analysis is also conduct ed to examine differences in actual student engagement (likes and comments) between the two experimental groups. 1.5 Research Contribution This research is significant because it utilizes Co mmunication Privacy Management theory (Petronio, 2002) and Instructiona l Humor Processing theory to

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6 expand our understanding of instructor self-disclos ure and use of humor via a coursebased social network. It also utilizes social prese nce theory to investigate the impact of instructor credibility on student engagement. The r esearch also contributes to the theory by providing an engagement model that is unique to online educational setting, by utilizing MooreÂ’s transactional distance theory, to study the moderating impact of time spent by student in the online social network. 1.6 The Thesis Outline The focus of this thesis is on engagement in course -based social networks. Chapter two will review the previous literature tha t has investigated our research problem. In chapter three we develop our research m odel and hypotheses. In chapter four, we present the exploratory study In chapter five, we present the main study. In chap ter six we discuss and interpret the results of our ana lysis. In this chapter we also conclude the thesis, present a discussion of the research co ntributions and implications for theory and practice, and discuss the study limitations and opportunities for future research.

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7 CHAPTER II LITERATURE REVIEW Computer-mediated communication (CMC) is defined as the process by which people create, exchange, and perceive information u sing networked telecommunications systems (or non-networked computers) that facilitat e encoding, transmitting, and decoding messages (December, 1996). CMC examples in clude e-mail, forums, chat, and online social networks (Herring, 2002). CMC modes h ave transformed organizational culture and interaction (Abbasi and Chen, 2008). CMC has provided invaluable support for various bus iness operations including organizational communication, knowledge disseminati on, transfer of goods and services, and product reviews (Turney and Littman, 2003; Coth rel, 2000). CMC has enabled online social network groups (Wenger and Snyder, 2000), vi rtual teams and group support systems (Abbasi and Chen, 2008), and networks of pr actice (Wasko and Faraj, 2005). These systems enabled companies to tap into the wea lth of information and expertise available across corporate lines, and facilitate or ganizational operations regardless of physical boundaries (Fjermestad and Hiltz, 1999; Mo ntoya-Weiss, Massey, and Song, 2001). However, little research has investigated th e potential impact of CMC on engagement in online social networks. In this secti on, the current literature on engagement is discussed. The current literature on communication that has the potential to build and support relationships is then discusse d, followed by a review of literature on credibility. Finally, the current literature on usi ng Facebook in education is discussed.

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8 2.1 Engagement Engagement with information technologies and system s is the feeling that a is the feeling that a system has caught, captured, and cap tivated user interest (Jacques, Preece, and Carey, 1995). System use, defined as the freque ncy, duration, and intensity of an employee’s interactions with a particular system (V enkatesh et al., 2003, Davis et al. 1989), is similar to involvement which may occur be cause of task demands or deadlines and thus may not be enjoyable (Sandelands and Buckn er, 1989). In contrast, engagement includes intrinsic interest. Therefore, there have been calls for research that help to create systems for which users’ interactions are pleasurab ly engaging, fun, and intrinsically motivating (Laurel, 1991; Malone and Lepper, 1987), to design systems to be more lively, intriguing, or fascinating (Giardina, 1992) and to recognize the achievement of engagement as an important goal in the design of sy stems (Mayes, 1992). Moreover, in the past few decades, human-computer interaction st udies have emphasized the need to move beyond usability to understand and design for more engaging experiences (Hassenzahl and Tractinsky, 2006; Jacques et al., 1 995; Laurel, 1993). Engagement is considered “a desirable -even essenti alhuman response to computer-mediated activities” (Laurel, 1993, p. 112 ). A Web interface that is boring, a multimedia presentation that does not captivate use rs’ attention or an online forum that fails to engender a sense of community is quickly d ismissed with a simple mouse click (O’Brien, 2008). Failing to engage users equates wi th no sale on an electronic commerce site and no transmission of information from a webs ite. People go elsewhere to perform their tasks and communicate with colleagues and fri ends (O’Brien, 2008). Engaging interactions are sought after by both users and dev elopers of computer systems and

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9 applications. Given the increased emphasis on user experience, it is no longer sufficient to ensure that technologies are merely usable (Blyt he, Overbeeke, Monk, and Wright, 2003). Successful technologies must engage users. In face-to-face educational settings, student engag ement was conceptualized as “a psychological process, specifically, the attention, interest, investment, and effort students expend in the work of learning” (Marks, 2000, P 154 ,155). There is a general agreement that engagement in learning is important for succes s (Klem and Connell, 2004) and is clearly an important component of the student exper ience. Research shows that student engagement in educational activities is positively related to learning, personal development and educational effectiveness (Klem and Connell, 2004). Research also links higher levels of engagement in school with im proved performance. For example, researchers have found student engagement a robust predictor of student achievement and behavior in school (Voelkl, 1995; Finn, 1993; Arhar and Kromrey, 1993; Mounts and Steinberg, 1995). Students engaged in school are mo re likely to earn higher grades (Goodenow, 1993; Willingham, Pollack, and Lewis, 20 02) and test scores, (Willingham, Pollack, and Lewis, 2002; Roderick and Engle, 2001) and have lower drop-out rates. (Connell, Halpcm-Kelsher, Clifford, Crichlow, and U singer, 1995) In contrast, students with low levels of engagement are at risk for a var iety of long-term adverse consequences, including disruptive behavior in clas s, absenteeism, and dropping out of school (Steinberg, Brown, and Dombusch, 1996; Finn, 1989; Lee, Smitb, and Croninger, 1995). The limited evidence to date about the relationship between student use of technology and student engagement, mostly from face -to-face educational settings, have

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10 affirmed the utility of information technology on: promoting student engagement (Hu and Kuh, 2001; Nelson Laird and Kuh, 2005; Robinson and Hullinger, 2008), and affecting a variety of outcomes such as student self-reported g ains in general education, personal development, and intellectual development (Hu and K uh, 2001; Kuh and Hu, 2001; Kuh and Vesper, 2001). However, some studies show mixed results. For example, Alavi (1994) and Oblinger and Maruyama (1996) provided ev idence that educationally purposeful use of information technology, such as e -mailing instructors or other students about assignments, does encourage collaboration amo ng students. Chen, Lambert, and Guidry (2010) found a positive relationship between Web-based learning technology use and student engagement and desirable learning outco mes. They found that students who utilize the Web and Internet technologies in their learning tend to score higher in the traditional student engagement measures. Similarly, Robinson and Hullinger (2008) found that asynchronous instructional technology pr ovide students with more time to think critically and reflectively, which in turns s timulates higher order thinking such as analysis, synthesis, judgment, and application of k nowledge. At the same time, Reisberg (2000) suggests that uses of information technology may distract students from participating in empirically confirmed effective ed ucational practices. Research by Atkinson and Kydd (1997); Dyck and Smit her (1994) and Whitley (1997) investigated student engagement in the onlin e educational environment, and have shown that experience with information technologies is associated with student engagement. This experience has been associated wit h spending more time in the online educational environment (Hiltz, 1994; Ridley and Sa mmour, 1994). This suggests that

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11 students who spend more time in the online educatio nal environment are more likely to be engaged in and satisfied with their own learning experience. In online education, research has found that for le arning to take place, online presentations should engage their audiences (Webste r and Ho, 1997; Jacques et al., 1995), and educators should critically engage stude nts with technology (Salvo, 2002). Although engagement represents an important issue f aced by instructors when communicating online with students, little empirica l research has addressed how best to improve student engagement in an online setting (Ma llon and Webb, 2000). Similarly, while the number of college courses being delivered via the internet is increasing rapidly, our knowledge of what makes these courses effective learning experiences that engage students is still limited (Arbaugh, 2000). The eval uation of online learning needs to go beyond traditional measures of studentÂ’s knowledge and learning and consider the quality of the learning experience as a whole (Robinson and Hullinger, 2008). Measures of student engagement offer such an evaluation. There are numerous differences between online setti ngs and face-to-face classrooms which can impact engagement. In online s etting students are more likely and have more opportunity to spend more time interactin g with other students and the instructor. However, educators also report challeng es engaging students in online work (Andrew Miller, 2012, Joanne M. Kossuth, 2011). The fundamental differences that exist between online interaction and face-to-face interac tion suggest there is a need to better understand how engagement can be improved in online settings. One way to build engagement in online setting is through the use of communication that builds and improves interpersonal and professional relationshi ps.

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12 This study investigates the nature of student engag ement in online educational environment to answer the questions of what promote s engagement in the online environment when using specific technology, online social networks, rather than studying the impact of using information technology in gener al. 2.2 Relationship Building Communication The role of communication in relationship building is crucial (Berger & Calabrese, 1975), and it is an essential part of bu ilding engagement in online social networks (Kodish & Pettegrew, 2008). Uncertainty Reduction Theory (URT) (Berger & Calabr ese, 1975) presumes that the beginning of interpersonal relationships is fra ught with uncertainties, and people want to reduce uncertainty in relationships through know ledge and understanding. Communicating directly with a person is one way to learn about each other and reduce uncertainty in relationships (Berger & Calabrese, 1 975). This suggests that communications increases knowledge about others, re duces uncertainty in relationships, and hence, builds relationships and engagement amon g members of online groups Two types of communication commonly used when building relationships are self-disclosure and humor. These two types of communication were se lected after looking at some real course-based social networks used by instructors to communicate with their students, mainly via Facebook and twitter. 2.2.1 Self-Disclosure. Wheeless & Grotz (1976) defined the self-disclosu re construct as ‘‘any message about the self that a pe rson communicates to another’’. Research in face-to-face environments suggests that people have higher satisfaction and

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13 feelings of trust and solidarity when they have rel ationships with higher levels of selfdisclosure (Wheeless, 1976, 1978; Wheeless & Grotz, 1977; Martin & Anderson, 1995; Martin, Anderson, & Mottet, 1997, 1999). Social Penetration Theory (SPT) (Altman and Taylor, 1973; Taylor, 1968; Taylor and Altman, 1975; Shaw and Costanzo, 1982) suggests tha t relational closeness and interpersonal communication progress from superfici al to intimate as relationships develop. It suggests that closeness develops throug h self-disclosure (Taylor and Altman, 1975). Self-disclosure stimulates feedback. The qua lity of the feedback is related to the amount and relevance of self-disclosure we receive and share with others. Self-disclosure increases with the need to reduce uncertainty in a relationship. Communication privacy management (CPM) theory (Petr onio, 2002) “offers a privacy management system that identifies ways privacy boun daries are coordinated between and among individuals” (Petronio, 2002, p. 3) and ‘‘sug gests a way to understand the tension between revealing and concealing private informatio n’’ (Petronio, 2007, p. 218) between and among those individuals. CPM is an evidence-bas ed theory about how people manage private information disclosure. CPM asserts that there are relational and personal needs, like engaging others, that are met by giving access or revealing private information. Self-disclosure has both benefits and risks (Metzge r, 2007). The benefits of disclosing private information include self-expression, social control, and the potential for improving interpersonal relationships (Petronio, 20 02; Taylor and Altman, 1975). The risks may include loss of face, status, control, or credibility (Metzger, 2007). CPM theory states that individuals develop rules to help them maximize the benefits of disclosure.

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14 These rules help individuals decide what, when, and whom to disclose (Petronio, 2002). However, CPM theory does not explain what factors m ake disclosure is associated with risk or associated with benefits. The relevance of self-disclosure has an impact on benefits and risks of self-disclosure, and hence on interpersonal relationships (Taylor and Altman, 1975). CPM has been utilized to explain self-disclosure is sues in personal relationships (e.g., Caughlin & Afifi, 2004; Mazur & Ebesu Hubbar d, 2004). (Rawlins, 2000) contended that the balance between self-disclosure and concealing of private information is especially important when considering the classr oom context. Principles of CPM theory can be utilized to investigate instructor pr ivacy management in the classroom context, where instructor-students relationship is public, yet instructors do disclose some private information. 2.2.2 Humor. Humor is defined as communication that involves m ultiple, incongruous meanings that are amusing in some manne r (Gervais and Wilson, 2005). S. Booth-Butterfield and Booth-Butterfield (1991) emph asized the intentional use of both verbal and nonverbal communication behaviors that e licit positive responses like laughter and joy in their definition of humor. Research provides some evidence that humor can be u sed appropriately in the classroom to enhance learning and student perceived learning outcomes. However, other research has demonstrated a negative impact of humo r on learning (e.g., Harris, 1989; Stuart & Rosenfeld, 1994; Torok, McMorris, & Lin, 2 004; Ziv, 1988). These conflicting results may be due to differences in the experiment al procedures. For example one study asked students to recall a class environment where humor has been used (Gorham &

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15 Christophel, 1990). Other studies used an artificia l experimental setting (Ziv, 1988). Another possible cause for differences in past expe rimental results may be that different types of humor were used or that the delivery mecha nism for the humor differed. For example, some studies have introduced humor through instructor lectures, cartoons, or audiotapes (Banas, Dunbar, Rodriguez & Liu, 2011). Research by Baxter & Wilmot (1984) and Graham (1995 ) indicated that a sense of humor facilitates the reduction of uncertainty i n interpersonal relationships and also serves to reduce social distance between interactan ts, and hence improves their engagement in a community. Humor is important in a variety of settings, including the development of social relationships (Alberts, 1990; Baxter, 1992). Humor is an engaging personality trait that has direct implications on b uilding interpersonal and professional relationships and communication (Graham, 1995). Peo ple at all relationship stages identify humor as a key factor in communication sat isfaction (Hecht, 1984), and relationship maintenance (Canary, Stafford, Hause, & Wallace, 1993). Incongruity theory provides evidence as to why humo r is useful in relationship building especially in educational settings. Incong ruity theory suggests that people find something humorous when they are required to resolv e incongruities in the message (Berlyne, 1960; Suls, 1972). The processing of thes e humorous incongruities can lead to a cognitive shift resulting from the sudden solutio n to the problem posed (Latta, 1999; Brian Boyd, 2004). The enjoyment gained from succes sfully resolving humor can lead to beneficial outcome. Research by Kurtzberg, Naquin, and Belkin (2009) de monstrates that the use of humor in communication results in increased trust a nd satisfaction levels, higher joint

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16 gains for the community, and higher individual gain s for the community member who initiated the humorous event. This suggests that th e individual who initiates humor is engaging others in the community, and improving the ir joint gains and individual outcomes. 2.3 Credibility Credibility refers to “the attitude of a receiver w hich references the degree to which a source is seen to be believable” (McCroskey 1998, p. 80). Instructor credibility, which is one of the most important variables affect ing the instructor-student relationship (Myers, 2001), is defined as the degree to which an instructor is perceived to know what he or she is talking about, the degree to which the instructor is perceived as honest, and the degree to which the instructor is perceived as to have the students’ best interests in mind (McCroskey and Teven, 1999). Researchers have identified instructor credibility as a critical factor in the learning process, “the hig her the credibility, the higher the learning” (Thweatt & McCroskey, 1998, p. 349). Rese arch shows that perceived instructor credibility matters to instructors and s tudents alike (Obermiller, Ruppert, and Atwood, 2012; Lavin, Davies, and Carr, 2010). Prior studies on instructor credibility have found when instructors are viewed as credible sources of knowledge and academic support, several important classroom outcomes are enhanced, including, but not limited t o, learning (Frymier and Thompson, 1992; Martin, Mottet, and Chesebro, 1997; McCroskey Valencic, and Richmond, 2004; Schrodt et al., 2009), student motivation to learn (Frymier and Thompson, 1992; Martin, Mottet, and Chesebro, 1997), communication between the instructor and student, both in

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17 and out of the classroom (Myers, 2004; Myers and Br yant, 2004), perceived teaching effectiveness (Myers, 2004), student perceptions of cognitive learning and affective learning (Johnson & Miller, 2002; Russ, Simonds, & Hunt, 2002; Teven & McCroskey, 1997), instructor affinity seeking behaviors (Frymi er & Thompson, 1992), instructor assertiveness and responsiveness (Martin, Chesebro, & Mottet, 1997), teacher immediacy (Thweatt & McCroskey, 1998), perceived instructor a rgumentativeness (Schrodt, 2003), and affect for the course and instructor (McCroskey et al., 2004). Students who consider their instructors to be credi ble recommend these instructors to their friends (Nadler & Nadler, 2001 ), feel understood by their instructors (Schrodt, 2003), evaluate both the class and their instructor more positively (Schrodt, 2003; Teven & McCroskey, 1997; Lavin, Davies, and C arr, 2010), are generally more satisfied (Obermiller, Ruppert, and Atwood, 2012), and are likely to take additional courses from them (Nadler and Nadler, 2001). Lavin, Davies, and Carr (2010) found credibility to have impacts on the studentÂ’s prepar ation for each class, attentiveness, appreciation for instructor effort, and respect for the instructor (Martinez-Egger and Powers, 2002). Nearly two decades ago, Frymier and Thompson (1992) noted that there was little research offering instructors advice on how to incr ease their credibility in the classroom, which established a new direction in research study ing instructor credibility. Consequently, instructional communication researche rs have devoted substantial efforts toward addressing the issue of offering instructors advice on how to increase their credibility. Some investigators have focused primar ily on instructor characteristics and communication behaviors that enhance credibility (e .g., Edwards & Myers, 2007; Martin,

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18 Chesebro, & Mottet, 1997; McCroskey, Valencic, & Ri chmond, 2004; Myers, 2001; Schrodt, 2003; Schrodt, Turman, & Soliz, 2006; Teve n, 2001; Semlak & Pearson, 2008), and instructor behaviors (McCroskey et al., 2004). In particular, instructors who use argumentative messages (Schrodt, 2003), verbal and nonverbal immediacy behaviors (Johnson & Miller, 2002; Teven & Hanson, 2004), aff inity-seeking behaviors (Frymier & Thompson, 1992), appropriate levels of technology u se (Schrodt & Turman, 2005; Schrodt & Witt, 2006), who are assertive and respon sive (Martin, Chesebro, & Mottet, 1997), who use nonverbal immediacy cues (McCroskey et al., 2004; Teven & Hanson, 2004), and who engage in out-of-class communication with their students (Myers, 2004) are generally perceived as being more credible in t he classroom. Other variables affecting instructor credibility include how the instructor d resses (Morris, Gorham, Cohen, & Huffman, 1996), the instructional format of the cou rse (Todd, Tillson, Cox, & Malinauskas, 2000), the aesthetic appeal of the ins tructorÂ’s office (Teven & Comadena, 1996), and the instructorÂ’s sex, race, age, and eth nicity (Hendrix, 1998; Patton, 1999; Semlak, Pearson, 2008). Conversely, instructor cred ibility is inversely associated with instructor misbehavior (Thweatt & McCroskey, 1998) such as perceived instructor verbal aggressiveness (Myers, 2001; Schrodt, 2003). Instructor credibility has been found to mediate th e effects of instructorsÂ’ prosocial communication behaviors on studentsÂ’ lear ning outcomes (Schrodt et al., 2009), mediate instructorsÂ’ classroom communication behavi ors (nonverbal immediacy, enthusiasm, and homophily) and studentsÂ’ intentions to persist in college (Wheeless, Witt, Maresh, Bryand, and Schrodt, 2011). Instructo r credibility has been also found to fully mediate the effects of immediacy and partiall y mediate the effects of instructor

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19 confirmation and clarity on learning outcomes (Schr odt et al., 2009). Enhanced credibility not only functions as a positive outcom e of effective classroom instruction but also mediates the effects of instructor behaviors t o student and classroom outcomes (Finn et al., 2009). Current research has focused on instructor credibil ity as both a product of instructor behaviors and as an antecedent to studen t learning outcomes in the college classroom (McCroskey et al., 2004). The concept and aspects of perceived credibility, its importance to the teaching experience, and the spec ific importance of communication behaviors in credibility impressions, have received substantial attention in instructor credibility research. However, little attention has been devoted to the role instructor credibility can play in engaging students especiall y in online educational environments. When studying instructor credibility in an online c ontext, there are several factors that can impact studentsÂ’ credibility perceptions. Previous studies (Fogg, 2002; Fogg and Marshall, 2001; Fogg and Tseng, 1999; Fogg, Marshal l, Laraki, Varma, Fang, Paul, Rangnekar, Shon, Swani, and Treinen, 2001; Johnson and Wiedenbeck, 2009) show that providing information about the author of online in formation as well as a picture enhances credibility. The types of information the instructor shares online can also impact credibility perceptions. Johnson (2011) exam ined whether posting social, scholarly, or a combination of social and scholarly information to Twitter has an impact on the perceived credibility of the instructor. She found that participants who viewed only the social tweets rated the instructor significantl y higher in perceived credibility than the group that viewed only the scholarly tweets. Myers, Brann, & Members of Comm 600 (2009) examined how college students consider their instructors to establish and enhance

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20 their credibility through their in-class self-discl osure. Witt and Kerssen-Griep (2011) investigated the combined effects of face-threat mi tigation and instructor nonverbal immediacy on perceived instructor credibility. Today, the communicative interaction opportunities between instructors and students are broadened, mainly through online socia l networks. Instructors report anecdotally that these technologies have increased the time, frequency, and breadth of instructor-student communication (Jacobs, 2004; Men zies & Newson, 2007; Osterlund & Robson, 2009). A generation ago, most communication occurred in the classroom or in office hours (Obermiller, Ruppert, and Atwood, 2012 ). Computer-mediated communication, like online social networks, has inc reased the time available for interacting among students, and between students an d the instructor. Computer-mediated communication demands an expansion of our understan ding of instructor credibility in an online educational environment, and its impact on s tudent engagement in such an environment. 2.4 Facebook in Education Social networks sites (such as Facebook, MySpace, T witter, etc.) are Internetbased CMC. Social networks sites are web-based serv ices that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the sys tem (Boyd and Ellison, 2007). Most of the online interactions via these social network sites were found to be between people who have also talked on the telephone or met face-t o-face, real life friends or colleagues

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21 (Miller & Slater, 2000). One domain where social ne twork sites are increasingly important is higher education. Social network sites allow students and their instructor to enhance their face-to-face interaction. Social netw ork sites can be used by instructor to engage students. Researchers argue that social netw ork systems should be a part of the classroom experience to support education communica tion, interaction, and relationships (Bosch, 2009). Facebook is the dominant social network site used i n education. It is distinctive from all other social network systems because it ha s stronger roots in the academic community (Downes, 2007), and is the largest social network site (Raphael, 2009). Students typically check their Facebook accounts mu ch more often than their schools’ online course administration software, eve n when this software provides chatrooms and discussion boards for synchronous and asy nchronous online discussions (Bosch, 2009). This suggests that it is not only th e functionality provided by Facebook that drives interaction, it is also the acceptance of such a network. Students even check their email and Facebook with approximately equal f requency (Roblyer, McDaniel, Webb, Herman, & Witty, 2010) which indicates the de gree to which Facebook is integrated into student daily lives. Facebook is providing the opportunity to enhance th e out-of-class communication. Out-of-class communication is define d as “instructor-student communication, occurring outside of the classroom s etting that demonstrates responsiveness to students’ needs” (Jones, 2008, p. 375). These interactions are often voluntary, yet most students report having some amo unt of out-of-class communication

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22 with their instructors (Fusani, 1994; Jaasma & Kope r, 1999). Out-of-class communication has been found to be positively relat ed to student motivation (Knapp & Martin, 2003; Jaasma & Koper, 1999). Waldeck, Kearn ey, and Plax (2001) found that students are more likely to communicate with those instructors online who utilize immediacy behaviors (e.g., use humor in classroom, or use Facebook in communication with students). The more students involve and get f eedback on their learning activities, or problem solving, the more adept they should become (Kuh, 2003; Shulman, 2002). Researchers have investigated whether Facebook and social networks sites pose a distraction from academic pursuits rather than a co nduit towards educational goals (Selwyn, 2009). Research has examined how students feel about having contact with their instructors on Facebook, and how this contact influ ences student perceptions of their instructors (Hewitt & Forte, 2006; Mazer, Murphy, & Simonds, 2009). The developing opportunities that Facebook can provide for educati on caused Facebook.com to ask developers to build new educational platforms to pr ovide collaboration and connectionsÂ’ tools in classrooms (Morin, 2007). This is in addit ion to the pages and groups that are already available in Facebook which can be used to support university courses. Mazer et al. (2007) compared Facebook to university -housed discussion boards and found that student interaction on Facebook is h igh, while interaction on a typical university discussion boards is more limited. This may be because university discussion boards are mostly static or because students expect a more professional website when they use the university-housed discussion boards. S tudies comparing student interaction rate on Facebook comparing to course management sys tems found that students preferred interaction on Facebook over the interaction on cla ssic course management systems

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23 (Kosik, 2007; Stutzman, 2008). Both Stutzman (2008) and Kosik (2007) reported that student preference for Facebook primarily stems fro m their familiarity and experience with Facebook, as well as from the immediate respon se they get when they need help. Studies investigating instructor motivations for us ing Facebook over other course management systems found a desire to meet students at their spaces and to break down barriers between themselves and students. Other res earch encouraged instructors to integrate Facebook into their university courses to foster critical thinking and allow students to create connections among their peers (B arnes, Marateo, and Ferris, 2007).

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24 CHAPTER III THEORETICAL BACKGROUND Initial research on the use of online social networ ks in education suggests they can be helpful. However, research has not addressed how instructors may best use these tools to engage students and improve their educatio nal outcomes. This study examines how self-disclosure (both course related and unrela ted), the use of humor via a coursebased social network, and instructor credibility ca n be used to improve student engagement, as well as to improve perceived educati onal outcomes, like student motivation to learn, and satisfaction with learning Engagement is the feeling that a system has caught, captured and captivated user interest (Jacques et al., 1995). Student engagement in a course-based social network means this course-based social network keeps the st udent totally absorbed in the browsing of content of this social network, holds t he student attention, excites the student curiosity, is fun, is intrinsically interesting, an d is generally engaging. 3.1 Self-Disclosure and Engagement One type of communication that can be used in a cou rse-based social network by instructors is self-disclosure. Research suggest th at when instructors personalize their teaching by talking about themselves, and telling s tories (Nussbaum, Comadena, & Holladay, 1987), it leads to improvements to the cl arity of the information presented for students (Downs, Javidi, and Nussbaum, 1988; Wambac h and Brothen, 1997), which held the student attention, improvements to the student perceptions of affective learning (Sorensen, 1989), which excited the student curiosi ty, and improvements in the students

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25 perceptions of their instructors ability to explain course content (Andersen, Norton, & Nussbaum, 1981; Bryant, Comiskey, Crane, & Zillman, 1980; Bryant, Comiskey, & Zillman, 1979; Civikly, 1986; Norton & Nussbaum, 19 81), which make absorbing these course content is intrinsically interesting. Holdin g the student attention, exciting the student curiosity, and the feeling of intrinsically interesting when absorbing course content are components of student engagement. Fusani (1994) claimed that instructor self-disclosu re is a rich personal source of student-teacher communication. Cayanus (2004) argue d for the use of instructor selfdisclosure as an effective instructional tool to fo ster student learning and make it intrinsically interesting. Gorham (1988) and McBrid e & Wahl (2005) contend that instructor self-disclosure behavior is a strategy t hat instructors can use to create an immediate classroom environment that encourages stu dentsÂ’ participation, and attract student attention. Self-disclosure used during outof-class communication allows for the disclosures to be more personalized and directly re lated to student's problems (Fusani, 1994). Communication behaviors of instructors, like their self-disclosure via a coursebased social network, influence student motives for communicating within the classroom (face-to-face community) and out-of-classroom (via the course-based social network), and increase student tendencies to communicate (Cay anus, Martin, and Weber, 2003; Myers, Mottet, & Martin, 2000; Mottet, Martin, & My ers, 2004), by making these communication are perceived as more interesting by the student. Prior research has investigated the impact of selfdisclosure on individual studentsÂ’ educational outcomes (e.g. Mazer, Murphy, and Simonds, 2007). However, it has only investigated the impact of the quantity of instructor self-disclosure on

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26 educational outcomes, and not the impact of differe nt types of self-disclosure. Past research in face-to-face environments indicates tha t the impact of instructor selfdisclosure is dependent on more than just the amoun t of self-disclosure made (Lannutti and Strauman, 2006). It suggests that types of info rmation disclosed will have different impacts (Cayanus & Martin, 2008). This study extend s prior research by examining the impact of two types of instructor self-disclosure v ia a social network: self-disclosure about instructor private information related to the course, e.g., work experience, and selfdisclosure about the instructorÂ’s private informati on unrelated to the course, e.g., personal life and beliefs. Communication privacy management theory (CPM) sugge sts that the decisions about whether and when to disclose private informat ion is rule-based (Petronio, 2002). These rules are formed based on a variety of criter ia, including culture, gender, contextual factors, risk-benefit ratio, and motivat ions. The same rules could be utilized by instructors, intentionally or unintentionally, to m anage their private information selfdisclosure. For instance, instructors may employ a motivation rule to evaluate their desire to engage students in the course-based social netwo rk and to improve student educational outcomes. However, a risk-benefit ratio rule also g overns the instructor self-disclosure. For example, disclosing about work experiences rela ted to the course may have a different risks and benefits than disclosing about beliefs unrelated to the course. CPM theory states that individuals develop rules to hel p them maximize the benefits of disclosure. These rules help individuals decide wha t, when, and whom to disclose (Petronio, 2002). However, CPM theory does not expl ain what disclosure factors are associated with risk or associated with benefits. T he type and relevance of self-disclosure

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27 has an impact on benefits and risks of self-disclos ure, and hence on interpersonal relationships (Taylor and Altman, 1975). Researchers investigating self-disclosure in tradit ional classrooms found that it can create an environment that encourages student p articipation (Goldstein & Benassi, 1994). However, researchers have also found that ce rtain topics should be avoided by instructors. Nunziata (2007) reported that an instr uctor's personal problems, personal opinions, and alcohol consumption are viewed by stu dents as inappropriate forms of instructor self-disclosure, which could negatively affect perceptions of instructor credibility, and make communication with this instr uctor is less interesting. Lannutti & Straumann (2006) argued that instructor self-disclo sure should not muddy the professional boundary between the instructor and th e student, or hold their attention off learning. Finally, research by Chaikin and Derlega (1974) suggests that self-disclosure of intimate and private information to a stranger, sel f-disclosure to an acquaintance, and self-disclosure to someone of a different age or po sition is less appropriate and more maladjusted than nondisclosure. Online communication has been shown to have higher levels of self-disclosure than seen in face-to-face communication (Des Jarlai s et al., 1999; Epstein, Barker, & Krotil, 2001; Lessler, Caspar, Penne, & Barker, 200 0). Researchers have found that there can be an online “dis-inhibition effect” that allow s some people to self-disclose more frequently or more intensely than they would in per son (Suler, 2004). There are a number of characteristics of online communication that can lead to increased sharing of personal information. One factor that has been found to incr ease self-disclosure online is the relative anonymity associated with online communica tion (Sobel, 2000; McKenna and

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28 Bargh, 2000, Joinson, 2001, Joinson, 2003). Another characteristic of online communication that can encourage enhanced informati on sharing is the fact that there are reduced nonverbal cues when communicating online (S uler, 2004; Walther, 1996). The fundamental differences between face-to-face commun ication and online communication have led to differing sets of privacy rules for the two different communication types. In fact, researchers have found that there are two dif ferent sets of norms (or privacy rules) governing offline and online self-disclosure and th ese norms are unrelated (Mesch and Baker, 2010). This is consistent with prior researc h investigating instructor communication via Twitter. Researchers found that i nstructors that post social information to twitter are perceived by students as being more credible than those who post more scholarly content (Johnson, 2011). Instructor self-disclosure to students via a course -based social network is a form of professional communication which can have higher risks related to information sharing than most social communication types. Accor dingly, communication privacy management theory suggests that personal informatio n should only be shared if there is a direct benefit to students that outweighs these ris ks. Thus the following hypotheses are proposed: H1: Self-disclosure via a course-based social netwo rk about topics directly related to the course will have a positive impact on studen t engagement in the course-based social network.

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29 H2: Self-disclosure via a course-based social netwo rk about topics unrelated to the course will have a negative impact on student e ngagement in the course-based social network. 3.2 Humor and Engaement Gorham and Christophel (1990) identified humor in a cademic context as an important immediacy behavior that can facilitate st udent learning, and positive perceptions of instructors, which may engage studen ts. Many researchers have investigated the impact of use of humor in teaching in face-to-face environments (Aylor & Opplinger, 2003; Bryant & Zillmann, 1988; Conkell Imwold, & Ratliffe, 1999; Davies & Apter, 1980; Downs, Javidi, & Nussbaum, 1988; Fry mier & Wanzer, 1999; Frymier & Weser, 2001; Gorham & Christophel, 1990; Kaplan & P ascoe, 1977; Sadowski & Gulgoz, 1994; Wanzer, 2002; Wanzer & Frymier, 1999a 1999b; White, 2001; Hauck & Thomas, 1972; Ziv, 1988). Other research has invest igated the impact of instructor use of humor on enhanced quality of the student-instructor relationship (Welker, 1977) and on affective learning (Wanzer & Frymier, 1999a). Baumgartner and Morris (2008) showed humor-based te aching is clearly more interesting for the students. Jaasma and Koper (199 9) found that instructor use of humor in teaching was superior as a predictor for formal and informal out of class communication between instructors and students, and make these communication more exciting for students. Milem and Berger (1997) foun d a positive relation between studentsÂ’ out of class communication with their ins tructors and their academic integration. Instructor use of humor reduces physic al and psychological distance with students in the classroom (Andersen, 1979), which m ake humor communication excite

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30 the student curiosity. Instructor immediacy resulti ng from the use of humor has been positively associated with student engagement (Chri stensen, Curley, Marquez, & Menzel, 1995; Menzel & Carrell, 1999). Recent research has distinguished between appropria te and inappropriate use of humor (Wanzer, Frymier, Wojtaszczyk, & Smith, 2006) Wanzer et al. (2006) identified four different categories of appropriate instructor use of humor (i.e., related humor, unrelated humor, self-disparaging humor, and unplan ned humor), similar to those identified in prior research (Bryant et al., 1979; Downs et al., 1988; Gorham & Christophel, 1990). Four other broad categories of inappropriate instructor humor were identified and labeled as offensive humor, disparag ing student humor, disparaging other humor, and self-disparaging humor (Wanzer et al., 2 006). Self-disparaging humor can be used positively as appropriate humor (e.g. instruct ors telling life stories that may have been embarrassing for them, or put them in an awkwa rd situation), and negatively as inappropriate humor (This type of humor involves a professor criticizing, poking fun of or belittling himself/herself. e.g. professor says, “I am such an idiot!” to the students). (See appendix A for details about Wanzer’s et al. c lassification of Instructor’s appropriate and inappropriate humor) Wanzer, Frymier, & Irwin (2010) suggested that the use of appropriate humor related to course material enhances student learnin g in the classroom. They proposed the Instructional Humor Processing Theory (IHPT) which offers an explanation for why some types of instructor-generated humor result in increased student learning and others do not. IHPT hypothesizes that humor related to ins tructional content correlates positively with student learning, while inappropria te form does not. However, IHPT

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31 investigated the impact of this humor on student le arning in general. In this research we extend IHPT by studying the impact of related and a ppropriate humor on engagement. Building on IHPT, instructor use of appropriate hum or (as defined by Wanzer et al. (2006)) via a course-based social network is expect ed to have a positive impact on student engagement in this course-based social netw ork. H3: Instructor use of humor via a course-based soci al network will have a positive impact on the student engagement in the course-base d social network. 3.3 Credibility and Engagement Instructor credibility is defined as the extent to which an instructor is considered to be believable, trusted by students, concerned ab out student welfare, and knowledgeable about a given subject matter (McCrosk ey, 1998). Teven and Hanson (2004) argue that an instructor who is perceived as a credible source is more likely to relate well with students, and to improve their edu cational outcomes. Scholars have noted that instructors who used behaviors to improve the clarity of the information presented to the student, do engage students while presenting co urse content (Downs et al., 1988). Methods viewed by students as a way to humanize ins tructors, make instructors appear approachable, and create affect for both the course and the instructor, can be used by the instructor to engage students (Nunziata, 2007). Credibility has been consistently related to positi ve affect for both the subject matter and instructors, and state motivation to lea rn (Frymier, 1994; Gorham, 1988). Affect toward instruction is a state of psychologic al and emotional arousal toward the instructor (Bloom, Englehart, Furst, Hill, and Krat hwohl, 1956). Affect is positively associated with studentsÂ’ motivation and learning ( Rodriguez, Plax, & Kearney, 1996).

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32 Students who have higher levels of affect generally exhibit approach behaviors toward the source of arousal and, as a result, are more en gaged (Titsworth, 2001). Short, Williams, and Christie (1976) presented soci al presence theory. Social presence is described as the feeling that the group members communicate with people instead of impersonal objects. Baker (2010, p. 5) a rgued “when communication channels are restricted, social presence decreases within a group. When social presence is low within a group, group members often feel disconnect ed and cohesion levels are low. When social presence is high, however, each group m ember has the feeling of joint involvement”. This suggest when instructor communic ates with student via a coursebased social network, if instructor’s credibility i s perceived as high by students, instructor’s social presence will be perceived as h igh by students; and students will be more engaged in this course-based social network. In face-to-face environments, instructors must be s een to be perceived as present (Picciano, 2002). In online environments, however, for the instructor to be perceived as present requires actions. Examples of these actions include, developing consistent patterns of interaction, communicating accessibilit y, providing consistent and substantive feedback, moderating discussions effectively, and p roviding content expertise through discussion posts to restart stalled discussions (Ar baugh and Hwang, 2006). These actions represent instructor social presence in online comm unication, and expected to have a direct impact on student engagement. These actions are more likely to be taken by the instructor who is perceived as highly credible. H4: Instructor Credibility will have a positive imp act on student engagement in a course-based social network.

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33 3.4 Engagement and Educational Outcomes Engaging systems have been described by users as: e nticing users (Mayes, 1992); drawing users into the activity (Laurel, 1991); and seducing and spurring users on (Skelly, 1991). When asked by Jacques et al. (1995) what engagement meant to them, users considered it to be a positive, interactive s tate, in which their attention was willingly given and held. They described their feel ings when interacting with engaging software as “curiosity, interest, confidence, and s urprise”. Users are engaged in a system when it "holds their attention and they are attract ed to it for intrinsic rewards" (Jacques et al. 1995, p. 58). Engaged users enjoy the activity or product, which may make them want to prolong the activity (Sandelands, 1988) or use t he product again (Jordan, 1998). Engagement is similar to flow, a state representing the extent of pleasure and involvement in an activity (Csikszentmihalyi, 1975) In the organizational behavior literature, employee engagement has been found to g enerate heightened morale, cohesion, job satisfaction, organizational commitme nt, citizenship behaviors, customer evaluations, reduced absenteeism, and consequently improved financial performance (Harter, Schmidt, & Hayes, 2002; Saks, 2006; Salano va, Agut, & Peiro, 2005). In a course-based social network, student engagement is a critical factor for student positive development (Casalo, Flavia, & Guinali, 2007; Koh a nd Kim, 2003). In higher education, student engagement has been defined as “how involve d or interested students appear to be in their learning and how connected they are to their classes, their institutions, and each other” (Axelson & Flick, 2011, p. 38). Student engagement is a desired behavior (Rocca, 2001), because it tends to improve student outcomes.

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34 Engagement in a face-to-face classroom environment has been demonstrated to be a positive indicator of educational outcomes. Howev er, engagement in a course-based social network, like a Facebook page for a course, is a more controversial issue. Interaction between instructors and students online via Facebook has spurred debate regarding its benefits and potential risks to stude nts (Nixon, 2011). It has the potential to provide for rich communication between students and instructors, but it is also a source of other types of communication that may negatively affect educational outcomes. Consequently, engagement in the Facebook IT artifac t, rather than engagement in faceto-face classroom environment, and its impact on th e educational outcomes is an area that needs more research. Specifically, this research ex amines two educational outcomes: motivation to learn, and satisfaction with learning 3.4.1 Impact of Engagement on Motivation to Learn. Past research indicates that motivation to learn is a robust predictor of c ourse outcomes and is influenced by both individual and situational characteristics (Colquit t, LePine, & Noe, 2000; Noe, 1986; Tannenbaum & Yukl, 1992; Noe & Schmitt, 1986; Quino nes, 1995). Bothun (1998) argues that the quality of learning depends on the student's level of motivation. Students who perceived their instructor as communicating cle arly and relevantly, and willing to interact outside of class time reported greater mot ivation (Chesebro & McCroskey, 2001; Jaasma and Koper, 1999). Course-based social networ ks provide students with the chance to know more about their instructor. When students know more about their instructor, they often express greater course motivation and vi ew the classroom climate more positively (Mazer, Murphy, & Simonds, 2007). Resear ch by Gorham and Millette (1997) suggests that student motivations can be sustained and diminished via classroom social

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35 communication. Allen, Witt, & Wheeless (2006) sugge st that competent instructors select and employ more innovative types of communication t o engage students with the expectation that students will respond favorably. Gorham (1988) and Dickmeyer (1993) found that instr uctor behaviors that engage students, created a more immediate (enjoyable) clas sroom environment, which is conductive to learning. Research also suggests that an immediate classroom environment is likely to enhance student motivation to learn (A ylor & Opplinger, 2003; Downs et al., 1988; Gorham & Christophel, 1992), this is because immediate classroom environments engage students more in their classes. Parrott (199 4) asserts that communication types that engage students can be used as a teaching stra tegy; it can promote understanding and increase attention and interest. Accordingly; it is hypothesized: H5: Engagement in a course-based social network wil l have a positive impact on the studentÂ’s motivation to learn. 3.4.2 Impact of Engagement on Satisfaction with Lea rning. Student satisfaction refers to the degree to which students are satisfied about interactions with an instructor (Frymier, 2005). Researchers have found that instructor behaviors, that engage students, lead students to perceive instructors as clear (Wambach & Brothen, 1997) and make them more satisfied with the course and the in structor. Goodboy (2009) found a positive relationship between the instructor clarit y and student satisfaction. Research by Opplinger (2003) and Martin (2007) found that prese nting educational materials in an engaging manner, including using tools that student s like and use in their everyday life, arouses positive emotions that become associated wi th learning. This leads to more positive attitudes towards education. Accordingly; it is hypothesized:

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36 H6: Engagement in a course-based social network wil l have a positive impact on the studentÂ’s satisfaction with learning. 3.5 Time Spent in the Online Social Network MooreÂ’s transactional distance theory (Moore, 1973; Moore and Kearsley, 1996) provides an explanation for why the use of online c ommunication tools may encourage interactions among students and the instructor in a n online environment. Moore (1973) asserted that the physical separation in distance e ducation leads to a potential misunderstandings and communication gap between the instructor and the student; however, increasing the time spent by student in th e online social network decreases this gap. The setting for MooreÂ’s transactional distance theory is distance education; however, it suggests that increasing the interaction time be tween instructor and students, by utilizing advances in online communication tools, l ike Facebook, may bridge the distance between students and the instructor in an online en vironment, which impacts the student engagement. In online settings, students are more likely and ha ve more opportunity to spend more time interacting with the classmates and the i nstructor than they do in a classroom. Social network tools can be used to increase the le vel of interaction, thus allowing students and instructors to reduce the psychologica l and physical distance between them and to foster psychological closeness through inter actions more than those offered by face-to-face setting (Lemak, Shin, Reed and Montgom ery, 2005). Impact of instructor credibility on engagement is a lso expected to be moderated by the amount of time spent in the online social ne twork. Studies have revealed that

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37 “relational satisfaction increases as people spend more time on-line and the number of messages helps to provide more information about on e's relational partner” (Wright, 2000, p 45). This suggests that the more time stude nts spend in an online social network, the more messages they are exposed to and thus, the more information they have about their instructor. This will enhance the impact of c redibility on engagement. We hypothesized that the type of communication used by the instructor; selfdisclosure and use of humor, via a course-based soc ial network, and instructor credibility have an impact on engagement in course-based social networks. However, building on Moore’s transactional distance theory, the level of engagement students will experience will be influenced by the amount of time they typic ally spend interacting in the online social network. Thus it is hypothesized that the im pact of self-disclosure, humor, and instructor credibility on student engagement in a c ourse-based social network will be moderated by the amount of time the student typical ly spends in that online social network. This suggests that students who spend more time int eracting in the online social network are more likely to be exposed to the instru ctor communication and engage with it than students who spend less time in the online soc ial network. Similarly, students are more likely to engage with instructor that they per ceive to be credible. But, the underlying factors influencing credibility, believa bility, trustworthiness, concern about student welfare, and subject matter knowledge (McCr oskey, 1998), all can be influenced by the students’ interaction with the instructor ov er time. Thus, a student who spends more time interacting in the online social network is expected to be more engaged when he/she perceives the instructor as more credible co mparing to another student who spends

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38 less time interacting in this online social network In summary, the relationships between the three independent variables (self-disclosure, h umor and instructor credibility), and the dependent variable (engagement) are expected to be stronger when the student spends more time interacting in the online social network. H7a: The impact of self-disclosure about topics rel ated to the course on student engagement in a course-based social network will be stronger when the student spends more time in the online social network. H7b: The impact of humor on student engagement in a course-based social network will be stronger when the student spends mo re time in the online social network. H7c: The impact of instructor credibility on studen t engagement in a course-based social network will be stronger when the student sp ends more time in the online social network. 3.6 Research Model Figure III.1 represents the research model being te sted in the thesis. This study investigates the impact of using self-disclosure an d humor via a course-based social network, and instructor credibility on student enga gement in this social network, and impact of this engagement on studentÂ’s perceived ed ucational outcomes, student motivation to learn and student satisfaction with l earning.

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Figure III.1 The Research Model To address the research questions posed by this In the exploratory study (Figure IV.2 combination of communication types (among self disclosure unrelated interests and use of humor) th at can be used by instructors via a coursebased social network to enhance student engagement in this social network. In the main study (Figure V.1 ), we conduct a semester long experiment where an i nstructor communicates with a class via a course T he experiment compares a pair of classes with one c lass receiving Facebook communication that includes instructor self related posts, and the other class (the control) re ceives only course related posts. The differenc e in the outcomes between the two groups will then be measured utilizing a The Research Model To address the research questions posed by this thesis two studies are conducted. the exploratory study (Figure IV.2 ), we conducted a survey to investigate the best combination of communication types (among self disclosure related interests, self disclosure unrelated interests and use of humor) th at can be used by instructors via a based social network to enhance student engagement in this social network. In the ), we conduct a semester long experiment where an i nstructor communicates with a class via a course based social network (Facebook page or group). he experiment compares a pair of classes with one c lass receiving Facebook communication that includes instructor self disclosure and humor along with course related posts, and the other class (the control) re ceives only course related posts. The e in the outcomes between the two groups will then be measured utilizing a 39 two studies are conducted. ), we conducted a survey to investigate the best disclosure related interests, self disclosure unrelated interests and use of humor) th at can be used by instructors via a based social network to enhance student engagement in this social network. In the ), we conduct a semester long experiment where an i nstructor based social network (Facebook page or group). he experiment compares a pair of classes with one c lass receiving Facebook disclosure and humor along with course related posts, and the other class (the control) re ceives only course related posts. The e in the outcomes between the two groups will then be measured utilizing a

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40 survey and measurements of the actual engagement of the students within the coursebased social network.

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41 CHAPTER IV EXPORATORY STUDY An exploratory study was conducted in which subject s were asked to read a simulated Facebook page for a specific course, and respond to survey questions related to their perceptions of the instructor self-disclosure and use of humor via this page. The study explores the impact of self-disclosure, via a social network, related to work experience, self-disclosure, via a social network, related to personal issues, and the instructor use of humor, via a social network over and above face-to-face community. One difference between this study and prior studies is the experimental treatment. Mazer, Murphy, and Simonds (2007), for example, operationa lized instructor self-disclosure, by disclosing information about the instructor, like p hotographs and biographical information, on a personal Facebook account and pro file. In this exploratory study, the instructor discloses about himself via a Facebook p age or group specifically created for the course. This type of self-disclosure is more li kely to be perceived as being targeted at the students in the course and, thus, is more likel y to be accessed by students. In addition, using Facebook pages and groups allow for posts spe cifically targeted at the course. Social norms have clearly demonstrated that inappro priate humor, e.g. sexual jokes, is not accepted in classroom, as such only appropriate typ es of humor (as defined by Wanzer et al. (2006)) were used as a part of this study. Figu re IV.1 represents the research model being tested in the exploratory study.

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Figure IV.1 4.1 Participants The participants are undergraduate students, enroll ed in an introduction to IS course at a Midwestern University. Students receive d extra credit for participating in the study; however, studen ts were asked but not required to participate in th is study 4.2 Manipulation The independent variables in this study; self experience, selfdisclosure about personal issues, and use of humor via a social network, are manipulated using posts in simulated Facebook p ages for a university course, these pos ts were posted by a fake instructor. We used a fake name for the instructor, so the students are not affected by the real instructor cr edibility. The Facebook pages include posts representing the different independent variab les, along with other posts abou course related topics. The page was designed to be similar to a normal Facebook page IV.1 The Research Model for the Exploratory Study The participants are undergraduate students, enroll ed in an introduction to IS course at a Midwestern University. Students receive d extra credit for participating in the ts were asked but not required to participate in th is study The independent variables in this study; self disclosure about related work disclosure about personal issues, and use of humor via a social network, are manipulated using posts in simulated Facebook p ages for a university course, these ts were posted by a fake instructor. We used a fake name for the instructor, so the students are not affected by the real instructor cr edibility. The Facebook pages include posts representing the different independent variab les, along with other posts abou course related topics. The page was designed to be similar to a normal Facebook page 42 Exploratory Study The participants are undergraduate students, enroll ed in an introduction to IS course at a Midwestern University. Students receive d extra credit for participating in the ts were asked but not required to participate in th is study disclosure about related work disclosure about personal issues, and use of humor via a social network, are manipulated using posts in simulated Facebook p ages for a university course, these ts were posted by a fake instructor. We used a fake name for the instructor, so the students are not affected by the real instructor cr edibility. The Facebook pages include posts representing the different independent variab les, along with other posts abou t course related topics. The page was designed to be similar to a normal Facebook page

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43 that might have been created for the simulated cour se. (See appendix B for a sample Facebook page used by the study). 4.3 Procedure There are eight different simulated Facebook pages for the same course, each with a different combination of posts representing the i ndependent variables. The participants are randomly directed to one of these treatments, p roducing random assignments of the participants to the treatment groups. The different “Facebook page” treatment options are as follow: Facebook page includes posts containing self-disclo sure about related work experience along with course related posts. Facebook page includes posts containing self-disclo sure about personal issues along with course related posts. 1) Facebook page includes posts containing humor along with course related posts. 2) Facebook page includes posts containing self-disclo sure about related work experience and self-disclosure about personal issue s, along with course related posts (no humorous posts). 3) Facebook page includes posts containing self-disclo sure about related work experience, and humor, along with course related po sts (no self-disclosure about personal issues). 4) Facebook page includes posts containing self-disclo sure about personal issues, and humor, along with course related posts (no self -disclosure about related work experience).

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44 5) Facebook page includes posts containing self-disclo sure about related work experience, self-disclosure about personal issues, and humor, along with course related posts (all of the options). 6) Facebook page includes course related posts only (c ontrol group). The total number of posts on each Facebook page is 12 posts, similar to the number of posts initially displayed on a normal Facebook page At the beginning of the survey, students were asked to read the posts in the simula ted Facebook page, and to suppose they are taking the mentioned course with the speci fic mentioned instructor. Then they were asked to respond to survey questions that meas ure the outcome variables along with manipulation check questions 4.4 Measurement The survey instrument was drafted using the literat ure pertaining to the constructs. The process included an exhaustive review of the re lated literature to derive the scales items for the constructs. There are four constructs in the exploratory study. Rather than developing new scales to measure these constructs, predefined and established measures that have been validated and utilized in previous r esearch is used in this study. The final questions included in the survey are presented in T able IV.1. Table IV.1: Measurement items used in the explorato ry study Construct Items Source Self-disclosure related. My instructor often posts her opinions about curren t course related events My instructor often posts about her attitudes towar d course related events occurring on campus My instructor often posts her opinion about course related events in the community Cayanus & Martin (2008)

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45 Table IV.1 (con't.) My instructor often shares her dislikes and likes related to the course content My instructor reveals relevant work experience in her posts Self-disclosure unrelated. My instructor often posts her opinions about current course events unrelated to the course My instructor often posts about her attitudes toward course unrelated events occurring on campus My instructor often posts her opinion about course unrelated events in the community My instructor often shares her dislikes and likes unrelated to the course content My instructor reveals personal information about herself in her posts Cayanus & Martin (2008) Use of Humor My instructor posts humor related to course material My instructor posts funny props to illustrate a concept or as an example My instructor posts jokes related to course content My instructor posts humorous story related to course content My instructor uses language in her posts in creative and funny ways to describe course material I found that the humor used by the instructor detract from the course experience The type and amount of humor used by this instructor encourages me to interact (comments/likes) on this Facebook page Frymier, Wanzer and Wojtaszczyk (2008) Engagement This Facebook page kept me totally absorbed in the browsing This Facebook page held my attention This Facebook page excited my curiosity This Facebook page was fun This Facebook page was intrinsically interesting This Facebook page was engaging Webster & Ho (1997)

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46 4.4.1 Self-Disclosure. Instructor’s Self-disclosure via a course-based s ocial network is measured using Cayanus and Martin’s (200 8) Instructor Self-Disclosure Scale (Appendix C). Items in the instructor disclosure sc ale are reworded to reflect instructor self-disclosure about course related or unrelated i ssues. Example of the items, “My instructor often posts about her attitudes toward c ourse related events occurring on campus”. Respondents indicate how well each item ap plies to their instructor using a seven-point response continuum, ranging from comple tely disagree (1) to completely agree (7). Negatively worded items were reverse cod ed in order to have higher scores reflect greater perceived instructor disclosure. Ca yanus and Martin (2008) reported an alpha reliability of .80 for “Instructor’s Self-dis closure” measure. In the present study, the “Instructor’s Self-disclosure” measure had a reliab ility of 0.90 and .95 for self-disclosure about related issues and self-disclosure about unre lated issues respectively. 4.4.2 Humor. Instructor use of humor via a course-based social network (appendix D) is measured by 7-items measure of inst ructor appropriate humor developed by Frymier, Wanzer and Wojtaszczyk (2008), based on the appropriate and inappropriate humor behaviors identified by Wanzer et al. (2006). The scale uses a 7-item Likert-type response set ranging from 1 (completely disagree) t o 7 (completely agree). Frymier, Wanzer and Wojtaszczyk (2008) reported an alpha rel iability of .85 for “use of humor” measure. In the present study, the “use of humor” m easure had a reliability of 0.93. 4.4.3 Engagement. Student engagement in the Facebook page is measur ed by 6items measure of engagement developed by Webster & Ho (1997) (appendix E). This measure asks participants to report on how much the y were engaged in the Facebook course page, and it was used in this study because it measures engagement in IT artifacts

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47 specifically, as this study is interested in engage ment in the Facebook page for the course. Responses were solicited using a 7-point Likert sca le ranging from 1 (completely disagree) to 7 (completely agree). Webster & Ho (19 97) reported an alpha reliability of .92 for “engagement” measure. In the present study, the “engagement” measure had a reliability of 0.92. 4.5 Instrument Validation A variety of validation checks were performed to as sess the appropriateness of the measures used. First, the data were checked for nor mality and outliers; the results of the check suggested there were no problems regarding no rmality or outliers in this study. Second, the scale items representing the constructs were assessed for content validity, convergent validity, and discriminant validity. Content validity represents the verification that t he method of measurement actually measures what it is expected to measure. C ontent validity is subjective and judgmental but is often based on two standards: Doe s the instrument contain a representative set of measures, and were sensible m ethods of scale construction used? (Flynn, Sakakibara, and Schroeder, 1995). In this study, the correspondence between the indiv idual items and the concept that these items are supposed to measure was consid ered. Several measures were taken to check for content validity: The survey instrument was reviewed by a number of r eputable individuals to help gauge content validity.

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48 The questionnaire was pre-tested, using a pilot stu dy, by gathering data from thirty randomly selected participants. These partic ipants were excluded from further analysis. Any inconsistencies or ambiguities were subsequentl y addressed. Minor revisions were made to the questionnaire as a result of the pretests. Convergent validity refers to a situation where ite ms that should be related are in reality related and correlate highly with one anoth er. Correlations between items that belong to the same construct were checked; results showed that convergent validity was achieved. Discriminant validity refers to a situation where t he loading of each item on its respective construct should be higher than its load ing on the other constructs in the model. This validity is tested by comparing the ave rage inter-scale correlations to the Cronbach alphas. Cronbach alphas should be greater than the average inter-scale correlations to achieve acceptable discriminant val idity (Karimi, Somers, and Gupta, 2001). This was the case for each of our measures i n this study. 4.6 Common Method Bias Tests Two primary ways were used to controlling for metho d biases; through (a) the design of the studyÂ’s procedures and (b) statistica l controls. In the design of the studyÂ’s procedure, some of the techniques recommended by Podsakoff, MacKenzie, Lee, and Podsakoff (2003), to controlling for method biases, where used in this study. First, different response formats were used for the measurement of the studyÂ’s variable. This should reduce biases in the retrieval stage of the response process by eliminating the saliency of any contextu ally provided retrieval cues. It should

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49 also reduce the respondentÂ’s ability and/or motivat ion to use previous answers to fill in gaps in what is recalled and/or to infer missing de tails. Second, procedures were used at the response editing or reporting stage. For exampl e, respondentsÂ’ answers were allowed to be anonymous. Another example was assuring respo ndents that there is no right or wrong answers and that they should answer questions as honestly as possible. These procedures should reduce peopleÂ’s evaluation appreh ension and make them less likely to edit their responses to be more socially desirable, lenient, acquiescent, and consistent with how they think the researcher wants them to re spond (Podsakoff et al., 2003). Third, the following recommendations of Podsakoff et al. ( 2003) were carefully considered. (a) defining ambiguous or unfamiliar terms; (b) avoidin g vague concepts and providing examples when such concepts must be used; (c) keepi ng questions simple, specific, and concise; (d) avoiding double-barreled questions; (e ) decomposing questions relating to more than one possibility into simpler, more focuse d questions; and (f) avoiding complicated syntax. It is possible that researcherÂ’s using procedural r emedies can minimize, if not totally eliminate, the potential effects of common method variance on the findings of their research. However, it is also useful to use s tatistical remedies that are available to control for common method biases. In this research, two statistical tests were used to test for common method bias. First, we perform HarmanÂ’s single-factor test twice, once including all independent variables and the depende nt variable and the other time including all independent variables. This method lo ads all items into an exploratory factor analysis with no rotation and with number of factors fixed at 1, to see whether one single factor does emerge or whether one general fa ctor does account for a majority of

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50 the covariance between the measures; if not, common method variances is not considered as a pervasive issue. The first part of Table IV.2 shows results when the all of the variables are included. The first factor explains o nly 29.385 % of the variance which is not a majority (Greene and Organ, 1973). The econd part of Table IV.2 shows results when the dependent variable is not included. The fi rst factor explains only 32.946 % of the variance which is not a majority (Greene and Or gan, 1973). Accordingly, one cannot conclude that common method variance is a concern. Table IV.2 Part-1: HarmanÂ’s singlefactor test, Total Variance Explained by one single factor when all of the variables are included. Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 5.877 29.385 29.385 5.877 29.385 29.385 Table IV.2 Part-2: HarmanÂ’s singlefactor test, Total Variance Explained by one single factor when the dependent variable is not included. Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.942 32.946 32.946 4.942 32.946 32.946 Second, as recommended by Padsakoff et al. (2003), we included in the PLS model a common method factor whose indicators inclu ded all the principal constructsÂ’ indicators to evaluate the size of common method va riance. The procedure we have followed is developed by Liang et al. (2007) and ha s been used by other authors (e.g.,

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51 Furneaux & Wade, 2011). We calculated each indicato rÂ’s variances substantively explained by the principal construct and by the met hod. The results demonstrate that indicator variance attributable to the common metho d factor range from 0.01% to 8% with an average of 3.2% and a median 2.8%, whereas indicator variance attributable to the underlying construct range from 54% to 81% with average of 70% and a median of 70%. The ratio of variance attributable to the unde rlying construct to that attributable to the common method factor is about 22:1. Given the s mall magnitude of method variance, we contend that the method is unlikely to be a seri ous concern for this study. 4.7 Exploratory Study Results The experiment was conducted using 402 subjects. Su bjects were randomly assigned to one of the eight treatment groups. Demo graphics are presented in Table IV.3. 93.7% of the subjects were less than 35 years old, and 50.6% of the subjects were male. Table IV.3 Sample Demographics Variable Frequency Variable Frequency Gender Male Female 50.6% 49.4% Having Facebook account Yes No 90% 10%. Age 18-24 25-34 35-44 45-55 55 and older 63.8% 29.9% 4.7% 0.8% 0.8% Frequency of visiting Facebook Not every day Once a day Twice a day Three or more times a day 31.3% 13.4% 15.4% 39.8% Level of education Some College Associates degree 52% 21.3% University level Freshman Sophomore 1% 15%

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52 Table IV.3 (conÂ’t.) BachelorÂ’s degree MasterÂ’s degree Doctorate 21.7% 4.3% 0.8% Junior Senior Graduate student 47% 20% 18% To evaluate the hypotheses model Partial Least Squa res (PLS) Structural Equation modeling (SEM) method was used. Item loadings, inte rnal consistency, and discriminant validity were used to evaluate the properties of th e research model. The loading of each indicator on its construct should have a path weigh t of at least 0.7 (Hulland, 1999). As can be seen in Table IV.4, all itemsÂ’ loading surpa ss this threshold. Table IV.4 Loadings and cross-loadings. Engagement Humor Self-Disclosure Related Self-Disclosure Unrelated Eng_1 0.81 0.34 0.17 -0.10 Eng_2 0.82 0.31 0.29 -0.17 Eng_3 0.89 0.39 0.29 -0.17 Eng_4 0.77 0.39 0.22 -0.07 Eng_5 0.88 0.42 0.28 -0.24 Hum_1 0.35 0.81 0.30 0.09 Hum_2 0.37 0.84 0.24 0.03 Hum_3 0.30 0.78 0.36 0.08 Hum_4 0.41 0.88 0.29 -0.05 Hum_5 0.40 0.83 0.38 0.06 SD1_1 0.22 0.32 0.74 -0.07 SD1_2 0.29 0.32 0.76 0.14 SD1_3 0.23 0.32 0.85 0.19 SD1_4 0.20 0.30 0.79 0.27 SD1_5 0.22 0.19 0.78 0.22 SD2_1 -0.18 0.02 0.14 0.90 SD2_2 -0.15 0.02 0.18 0.90 SD2_3 -0.13 0.12 0.22 0.88

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53 Table IV.4 (conÂ’t.) SD2_4 -0.16 0.02 0.22 0.88 SD2_5 -0.20 0.05 0.10 0.88 Each construct's composite reliability score was us ed to evaluate internal consistency. The composite reliability scores (left most column of Table IV.5) all exceed 0.7 and thus are adequate for each construct (Hair et al, 1998). Discriminant validity evaluating has two parts; firstly, the loading of e ach item on its respective construct should be higher than its loading on the other cons tructs in the model, and secondly, the Square Root of Average Variance Extracted (Square R oot of AVE) for each construct should be higher than the inter-construct correlati ons (Agarwal & Karahanna, 2000). In table 4, by comparing the loading of each item on i ts respective construct to the other cells in the same row, we can see that all items lo ad higher on their respective construct than the other constructs in the research model. Li kewise, in Table IV.5, by comparing the constructsÂ’ Square Root of AVE on the diagonal to the inter-construct correlations on the other cells, we can see that the Square Root of AVE for each construct is higher than the inter-construct correlations without exception. These two comparisons suggest that the model has good discriminant validity. Table IV.5 Internal consistency and discriminant va lidity. Composite Reliability Square Root of AVE and inter-construct correlations Engagement Humor Self-disclosure Related Self-disclosure Unrelated 0.92 Engagement 0.84 0.92 Humor 0.44 0.83 0.89 Self-disclosure Related 0.30 0.38 0.79 0.95 Self-disclosure Unrelated -0.19 0.05 0.19 0.89

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54 The results of the PLS SEM analysis are presented i n Figure IV.3. Engagement had an R-Square of .277. This means that 27.7% of t he variance in engagement is explained by self-disclosure related interests, sel f-disclosure unrelated interests, and use of humor via a social network collectively (Agarwal & Karahanna, 2000). The path coefficients between self-disclosure unrelated, hum or and engagement were significant at .01, while the path coefficients between self-discl osure related and engagement was significant at .05. As summarized by Table IV.6, al l three hypotheses were supported. Table IV.6: Summary of hypotheses tests. Hypothesis Supported H1: Self-Disclosure Course Related Engagement Yes H2: Self-Disclosure Course Unrelated Engagement Yes H3: Humor Engagement Yes Figure IV.3 PLS SEM Results. Significant at .05 ** Significant at 01

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55 CHAPTER V MAIN STUDY An experimental study was conducted in which subjec ts are engaged in a real course-based social network, via Facebook page/grou p throughout a semester. In this experiment instructors were provided with the chanc e to publish actual posts related to the independent variables treatment resulted in the exploratory study, self-disclosure about related interests and use of humor. The diffe rence between independent variables among the experimental groups (control and test gro ups) was measured. Figure V.1 shows the revised model conducted in the main study. Hypothesis 2 about the negative impact of self-disclosure via co urse-based social network about unrelated interests on the student engagement was d ropped, because the results from the exploratory study indicate that there is a negative impact for self-disclosure via coursebased social network about unrelated interests on t he student engagement. Consequently, self-disclosure about unrelated interests may detra ct from learning thus will not be extended to a real class environment. Hypothesis 4 about the impact of instructor credibility on student engagement was added. Hypoth esis 4 was not included in the exploratory study, as students did not have a prior vision of credibility for the fictional instructor so it does not fit before engagement in the exploratory study. One addition to the research model in the main stud y is examining the impact of engagement on educational outcomes. This addition i mproves contributions of this research, because it examines the possible impact o f the studyÂ’s variables on the field where this study applied.

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56 Two traditional educational outcomes are examined i n this study: student motivation to learn, and student satisfaction with learning. Student motivation to learn refers to student attempts to obtain academic knowl edge or skills from classroom activities by finding these activities meaningful ( Brophy, 1987). Student satisfaction refers to the degree to which students are satisfie d about learning the course content and about interactions with an instructor (Frymier, 200 5). These learning outcomes were chosen for three reaso ns. First, these variables are traditionally studied as important outcomes in the classroom by instructional communication scholars because they are representat ive of student achievement. Second, instructor use of communication directly influences these outcomes (Kelley and Gorham, 1988; Richmond, Gorham, and McCroskey, 1987). Third instructor confirmation (e.g. instructor use of SNS in communication with student s) is one positive instruction behavior already associated with student motivation (Ellis, 2000, 2004) and may also be associated with the additional learning outcomes of student satisfaction. Ellis (2000, p. 287) directly advised that future researchers shoul d examine student satisfaction with instructor confirmation behaviors. The other addition in the revised model is adding t hree hypotheses about the moderating impact of time spent in the online socia l network. These three moderating hypotheses were not investigated in the exploratory study, as the students didnÂ’t engage in a real course-based social network so we can inv estigate amount of time they spent interacting online.

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Figure V.1 : The Revised Research Model used in the Main Study 5.1 Sample A sample of 266 undergraduate students enrolled in eight different courses taught by three different instructors at the University of Colorado Denver are used for the main study. Each one of the three instructors has simila r number of participants among th control and test groups in order to control for cro ss course variation. Students were asked but not required to participate in the study and ar e assigned extra credit for participating in the study. Four of these courses were assigned t o the test group; were assigned to the control group. The experiment utilized pairs of courses taught by the same instructor as the experimental and control con ditions to minimize variation in outcomes caused by variation in teaching style or c ourse conte be conducted to test if there is any significant di fference in the independent variables reported by the participants among the different cl asses in the control or experimental : The Revised Research Model used in the Main Study A sample of 266 undergraduate students enrolled in eight different courses taught by three different instructors at the University of Colorado Denver are used for the main study. Each one of the three instructors has simila r number of participants among th control and test groups in order to control for cro ss course variation. Students were asked but not required to participate in the study and ar e assigned extra credit for participating in the study. Four of these courses were assigned t o the test group; four other courses were assigned to the control group. The experiment utilized pairs of courses taught by the same instructor as the experimental and control con ditions to minimize variation in outcomes caused by variation in teaching style or c ourse conte nt. A validation check will be conducted to test if there is any significant di fference in the independent variables reported by the participants among the different cl asses in the control or experimental 57 : The Revised Research Model used in the Main Study A sample of 266 undergraduate students enrolled in eight different courses taught by three different instructors at the University of Colorado Denver are used for the main study. Each one of the three instructors has simila r number of participants among th e control and test groups in order to control for cro ss course variation. Students were asked but not required to participate in the study and ar e assigned extra credit for participating four other courses were assigned to the control group. The experiment utilized pairs of courses taught by the same instructor as the experimental and control con ditions to minimize variation in nt. A validation check will be conducted to test if there is any significant di fference in the independent variables reported by the participants among the different cl asses in the control or experimental

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58 groups. This will help determine if there are any s ystematic biases due to differences in the instructors and classes. If the validation chec ks indicate the sample is not biased, results should be generalizable to studentsÂ’ popula tion with similar characteristics. Table V.1 explains the courses used in this study. Table V.1: University courses from which sample dra wn from Course Number of participants Instructor Control/Test Group Introduction to IS 35 Instructor 1 Test Group Introduction to IS 31 Instructor 1 Test Group Introduction to IS 35 Instructor 1 Control Group Introduction to IS 36 Instructor 1 Control Group Introduction to IS 35 Instructor 2 Control Group Introduction to IS 31 Instructor 2 Test Group Introduction to IS 33 Instructor 3 Control Group Introduction to IS 30 Instructor 3 Test Group Total number of participants: 266 / Control group: 139 (47.7%) / Test group: 127 (52.3%) 5.2 Experiment Design This study employed an experimental design to inves tigate the impact of instructor self-disclosure about related interests, instructorÂ’s use of humor via coursebased social network and instructor credibility on student engagement in this coursebased social network, and the impact of student eng agement on student perceived educational outcomes. In this experiment there are two groups of participants. In the first

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59 group, the test group, instructors communicated wit h students by posting private information, about his private experience, but rela ted to the course content, and by posting humorous posts, in addition to course relat ed posts. In the second group, the control group, instructors communicated with studen ts by posting university and course related posts only. Each experimental group partici pated in the experiment for an entire semester. At the end of the experiment, participant s in each group completed a survey that measures the study outcomes. In addition, actu al participant engagement within the course-based social network (the Facebook page or g roup) was collected and recorded. 5.3 Procedure The independent variables in this study, self-discl osure and use of humor, are manipulated using Facebook posts representing these variables. In the experimental group, participated instructors created Facebook pa ge or group to communicate with the students. Then they posted private information rela ted to the course, in addition to humorous posts. In both experimental and control gr oups instructors posted announcements and materials related to the course. The Facebook pages or groups are real ones. Students were asked to join this page/gr oup and were encouraged to be engaged in it by making comments, likes, and posts. At the end of semester, a survey was conducted to test for the difference in the outcome variables, engagement and educational outcomes. Actual engagement data was also recorded. 5.4 Measurement Similar to the exploratory study, the survey instru ment was drafted using previously validated instruments. The same measures related to self-disclosure, humor

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60 and engagement, are used in the main experiment as were used in the exploratory study. Table V.2 shows the measures used in the main study This survey was conducted at the end of the semester. Data about the actual engageme nt within the test and control groups was also collected from the Facebook pages or group s for post-hoc analysis in this study. This actual engagement data includes data about the number of comments, number of likes, number of posts, and types of posts made by students. Table V.2 Measurement items used in the main study Construct Items Source Self-Disclosure related. My instructor often posts her opinions about curren t course related events My instructor often posts about her attitudes towar d course related events occurring on campus My instructor often posts her opinion about course related events in the community My instructor often shares her dislikes and likes r elated to the course content My instructor reveals relevant work experience in h er posts Cayanus & Martin (2008) Use of Humor My instructor posts humor related to course materia l My instructor posts funny props to illustrate a con cept or as an example My instructor posts jokes related to course content My instructor posts humorous story related to cours e content My instructor uses language in her posts in creativ e and funny ways to describe course material I found that the humor used by the instructor detra ct from the course experience The type and amount of humor used by this instructo r Frymier, Wanzer and Wojtaszczyk (2008)

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61 Table V.2 (conÂ’t.) encourages me to interact (comments/likes) on this Facebook page Credibility Intelligent/Unintelligent Expert/Inexpert Competent/Incompetent Informed/Uninformed Stupid/Bright Trained/Untrained Teven and McCroskey (1997) Engagement This Facebook page kept me totally absorbed in the browsing This Facebook page held my attention This Facebook page excited my curiosity This Facebook page was fun This Facebook page was intrinsically interesting This Facebook page was engaging Webster & Ho (1997) Motivation Motivated / Un-Motivated Interested / Uninterested Involved / Uninvolved Excited / Not Excited Looking forward to it / Dreading it Richmond (1990) Satisfaction Dissatisfied / Satisfied Displeased / Pleased Content / Discontent Frymier & Houser (1998) Following are more details about the measures added for the main study. 5.4.1 Creibility. Credibility is measured using Teven and McCroskey Â’s (1997) measure of instructor credibility (Appendix F). The instrument is composed of 5, sevenstep semantic-differential scales. It asks particip ants to evaluate their instructor.

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62 Responses were solicited using a 7-point bipolar ad jective scale. Teven and McCroskey’s (1997) reported an alpha reliability of .95 for “in structor credibility” measure. 5.4.2 Motivation to Learn. Student’s motivation to learn is measured by 5-it ems measure of student’s motivation to learn developed by Richmond (1990) (Appendix G). It asks participants to report on their levels of s tate motivation toward a specific course and instructor. Responses were solicited using a 7point bipolar adjective scale. Richmond (1990) reported an alpha reliability of .9 4 for “motivation to learn” measure. Previous reliability coefficients ranging from .89 to .93 have been reported (Myers & Zhong, 2004; Weber et al., 2005). 5.4.3 Satisfaction with Learning. Student’s satisfaction with learning is measured by 3-items measure of student’s satisfacti on with learning developed by Frymier & Houser (1998) (Appendix H). It asks parti cipants to report on their feelings of satisfaction with their instructor and course. Resp onses were solicited using a 7-point bipolar adjective scale. Previous reliability coeff icients ranging from .92 to .95 have been reported for the summed scale (Frymier, 2005; Frymi er & Houser, 1998; Myers & Bryant, 2002). 5.5 Main Study Results The experiment was conducted using 266 subjects. De mographics are presented in table V.3 97% of the subjects were less than 35 years old, and 54.6% of the subjects were male. To evaluate the hypotheses model Partial Least Squa res (PLS) Structural Equation modeling (SEM) method was used. Item loadings, inte rnal consistency, and discriminant validity were used to evaluate the properties of th e research model. The loading of each

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63 indicator on its construct should have a path weigh t of at least 0.7 (Hulland, 1999). As can be seen in Table V.4, all itemsÂ’ loading surpas s this threshold. Table V.3 Sample Demographics Variable Frequency Variable Frequency Gender Male Female (54.6%). (45.4%). University level Freshman Sophomore Junior Senior Graduate student (3.62%). (29.93%). (45.07%). (18.42%). (2.96) Age 18-24 25-34 35-44 45and older (70.7%). (26.3%). (2.6%). (0.4%). Level of education Some College Associates degree BachelorÂ’s degree MasterÂ’s degree or higher (65.13%). (22.04%). (11.84%). (.66%). Table V.4 Loadings and cross-loadings. Credibility Engagement Humor Motivation Satis faction SelfDisclosure Cred1 0.88 0.44 0.46 0.42 0.32 0.36 Cred2 0.91 0.50 0.48 0.45 0.38 0.41 Cred3 0.88 0.48 0.45 0.41 0.27 0.41 Cred4 0.80 0.40 0.37 0.41 0.25 0.28 Cred5 0.84 0.43 0.38 0.37 0.23 0.38 Eng1 0.41 0.77 0.41 0.40 0.40 0.45 Eng2 0.43 0.85 0.45 0.44 0.43 0.49 Eng3 0.47 0.90 0.48 0.44 0.47 0.49 Eng4 0.46 0.86 0.49 0.41 0.46 0.46 Eng5 0.45 0.88 0.45 0.40 0.43 0.39 Eng6 0.47 0.87 0.49 0.39 0.43 0.45 Hum1 0.36 0.48 0.86 0.32 0.39 0.49 Hum2 0.41 0.47 0.88 0.35 0.37 0.43 Hum3 0.43 0.39 0.81 0.36 0.35 0.45 Hum4 0.47 0.50 0.89 0.43 0.40 0.45 Hum5 0.46 0.44 0.80 0.35 0.34 0.42 Motiv1 0.44 0.42 0.37 0.84 0.35 0.27

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64 Table V.4 (conÂ’t.) Motiv2 0.43 0.39 0.38 0.86 0.37 0.27 Motiv3 0.34 0.40 0.32 0.76 0.24 0.24 Motiv4 0.47 0.47 0.42 0.98 0.39 0.31 Motiv5 0.34 0.37 0.31 0.79 0.36 0.24 SD1_1 0.32 0.40 0.48 0.21 0.35 0.75 SD1_2 0.42 0.46 0.43 0.38 0.36 0.78 SD1_3 0.39 0.41 0.47 0.29 0.37 0.86 SD1_4 0.31 0.44 0.44 0.21 0.33 0.85 SD1_5 0.31 0.45 0.35 0.19 0.29 0.83 Satis1 0.34 0.47 0.42 0.39 0.92 0.41 Satis2 0.31 0.47 0.41 0.37 0.93 0.38 Satis3 0.30 0.48 0.38 0.36 0.92 0.36 Construct's composite reliability scores were used to evaluate internal consistency. The composite reliability scores (left most column of Table V.5) all exceed 0.7 and thus are adequate for each construct (Hair et al, 1998). Discriminant validity evaluating has two parts; firstly, the loading of e ach item on its respective construct should be higher than its loading on the other cons tructs in the model, and secondly, the Square Root of Average Variance Extracted (Square R oot of AVE) for each construct should be higher than the inter-construct correlati ons (Agarwal & Karahanna, 2000). In table V.4, by comparing the loading of each item on its respective construct to the other cells in the same row, we can see that all items lo ad higher on their respective construct than on other constructs in the research model. Lik ewise, in Table V.5, by comparing the constructsÂ’ Square Root of AVE on the diagonal to t he inter-construct correlations on the other cells, we can see that the Square Root of AVE for each construct is higher than the inter-construct correlations without exception. The se two comparisons suggest that the model has good discriminant validity.

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65 Table V.5. Internal consistency and discriminant va lidity. Composite Reliability Square Root of AVE and inter-construct correlations Credibility Engagement Humor Motivation Satisfactio n SelfDisclosure .94 Credibility 0.74 .94 Engagement 0.47 0.74 .93 Humor 0.47 0.49 0.72 .89 Motivation 0.46 0.46 0.44 0.7 .95 Satisfaction 0.37 0.48 0.43 0.42 0.85 .91 Self-Disclosure 0.46 0.48 0.48 0.35 0.41 0.7 The results of the PLS SEM analysis are presented i n Figure V.2. Engagement had an R-Square of .427. This means that 42.7% of t he variance in engagement is explained by credibility, self-disclosure, and use of humor via a social network collectively (Agarwal & Karahanna, 2000). Motivatio n had an R-Square of .233. This means that 23.3% of the variance in motivation is e xplained by engagement. Satisfaction had an R-Square of .259. This means that 25.9% of t he variance in satisfaction is explained by engagement. The path coefficients betw een credibility, self-disclosure, use of humor and engagement were significant at .05, wh ile the path coefficients between engagement, motivation, and satisfaction were signi ficant at .01. Results are summarized by Table V.6, all five hypotheses were supported. Table V.6 Summary of hypotheses tests. Hypothesis Supported H1: Self-Disclosure Course Related Engagement Yes H3: Humor Engagement Yes H4: Credibility Engagement Yes H5: Engagement Motivation Yes H6: Engagement Satisfaction Yes

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66 Figure V.2 PLS SEM Results. Significant at .05 ** Significant at .01 5.5.1 Moderating Impact of Time Spent in Online Soc ial Networks. To check for the moderating impact of time spent by students in the online social network, we used multi-group moderation. The dataset was split into two parts based on the moderating variable (time spent in the online social network), we checked the model two times, each time with one of the two sub-samples, and then we c hecked whether there is significant difference between the two results using a t-test. The first sub-sample included participants who spen d little time (low time) in the online social network (less than the average time r eported by students in the original sample). The second sub-sample included participant s who spent a lot of time (high time) in the online social network (more than 60 minutes per day). We found significant difference between the low and high sub-samples.

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67 Comparing the relationship between self-disclosure and engagement of students that spent little time on the online social network s with those that spent more than 60 minutes per day at the online social network we see a significant difference between these two groups. When students spent little time in the online social network; self-disclosure had an insignificant impact on engagement, Figure V .3. While when students spend a significant amount of time in the online social net work; self-disclosure had a significant impact on engagement, Figure V.4. A t-test was used to check if there is a significant difference between these two results. It shows the re is a significant difference between the two results; P < 0.05. See Table V.7 Thus, hypo thesis 7a is supported. Comparing the relationship between humor and engage ment of students that spent little time on the online social networks with thos e that spent more than 60 minutes per day at online social networks we see a significant difference between these two groups. When students spent less time in the online social network; humor had an insignificant impact on engagement, Figure V.3. While when studen ts spent a lot of time in the online social network; humor had a significant impact on e ngagement, Figure V.4. When we used a t-test to check if there is a significant di fference between these two results we found that there is a significant difference betwee n the two results. P < 0.05, table V.8. Thus, hypothesis 7b is supported. Comparing the relationship between credibility and engagement of students that spent little time on the online social networks wit h those that spent more than 60 minutes per day at online social networks we see a differen ce between these two groups, however, this difference is insignificant. When students spe nt little time in the online social network, credibility had a significant impact on th eir engagement, Figure V.3. However,

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68 when students were already spending a significant a mount of time in the online social network, credibility had an insignificant impact on engagement, Figure V.4. A t-test revealed that there is not a significant difference between the two results. P = 0.19, Table V.9. Thus, hypothesis 7c is not supported. Figure V.3 PLS SEM Results for Low “Time Spent” Gro up.

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69 Figure V.4 PLS SEM Results for high “time spent” gr oup. Table V.7 T-Test Results for Self-Disclosure Engagement Group1 “Low time spent” Group2 “High time spent” Sample Size 77 100 Regression Weight 0.096 0.349 t-statistic 2.406 p-value 0.017 Table V.8 T-Test Results for Humor Engagement Group1 “Low time spent” Group2 “High time spent” Sample Size 77 100 Regression Weight .11 .595 t-statistic 2.308 p-value 0.022

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70 Table V.9 T-Test Results for Credibility Engagement Group1 “Low time spent” Group2 “High time spent” Sample Size 77 100 Regression Weight 0.435 0.56 t-statistic 1.315 p-value 0.190 5.5.2 Post-Hoc Analysis. Content analysis was used to classify the posts published by the three participated instructor into the posts types presented in Table V.10. Three different categories of posts were defined; s elf-disclosure about course related interest posts, use of humor posts, and course rela ted posts, as shown in Table V.10. All 112 posts were classified by one of the authors and a graduate student familiar with the use of Facebook in education. The graduate student was provided with the classification scheme, a description of each of the complaint cate gories, and an example of a post that would correspond to each classification type. Some post examples were used as a training sample to ensure that both coders agreed on the int erpretation of the complaint types in ensure that both coders agreed on the interpretatio n of the post types in Table V.10. Once both coders agreed on the interpretation of the pos ts’ types, and both are comfortable with the classification scheme, they independently classified the remaining 112 published posts. When the classification was completed, a Kap pa value of 0.94 (p < 0.000) was computed and used as an index of inter-coder reliab ility (Cohen, 1960). Since this value exceeded 0.80, the reliability of the coding was de emed acceptable (Grazioli & Jarvenpaa, 2003). After the coders completed the in dependent coding, they met to discuss each case where they disagreed and selected a mutua lly agreeable coding.

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71 Table V.10 The Classification Scheme for Posts Cate gories Post Category Description of Post Category Example Self-disclosure related interest When instructor posts private information, but related to the course interest. Like when instructor posts about work experiences related to course. One of the main tasks I was responsible for in my career was to keep track of all transactions that occur in the business. I used to use Excel to create a “journal” file; we’ll discuss this in the next class Use of humor When instructor posts appropriate humorous posts, like jokes and comic arts related to the course. Whoever is planning to not attend the next exam, please prepare yourself to be as smart as the book author for the makeup exam Course related posts When instructor posts course related interests. Like when instructor posts announcement or further course explanations. Word 2010, like its predecessor, also has a Mini toolbar that will pop up when you select text for editing. This Mini toolbar is a quick and simple means for simple formatting and editing After classifying all of the published posts, actua l students’ engagement data, represented by number of likes and comments made by them, will be assigned each published post in a data point for the content anal ysis in the post hoc analysis, however number of likes and comments is divided by the numb er of students who joined the course-based social network to be standardized. The post hoc analysis focuses on two different comp arisons. First, a comparison of the average engagement per course-based social n etwork will be considered to determine if students demonstrate more actual engag ement in courses that include posts that contain instructor personal information and hu mor than the courses that only posts about course related topics. Second, within the cou rses a comparison of engagement activity will be measured across the different type s of posts to determine which type of

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72 post generates the most engagement. The post hoc an alysis also provides additional validation for the results found in the survey. Table V.11 and Figure V.5 show a comparison of aver age studentsÂ’ engagement represented by number of likes and comments made by students per course-based social network. Table V.12 and Figure V.6 show a compariso n of average studentsÂ’ engagement represented by number of likes and comments made by students per experimental group (test and control groups) by combining the courses that belong to the same experimental group. Table V.13 and Figure V.7 show a comparison of average studentsÂ’ engagement represented by number of likes and comments made by students per post type (selfdisclosure, humor, and course posts). Table V.11 Average engagement per a course-based so cial network # of Likes # of Comments Control 1 4% 1% Control 2 2% 1% Control 3 1% 0% Control 4 1% 1% Test 1 32% 9% Test 2 16% 3% Test 3 22% 2% Table V.12 Average engagement per an experimental g roup # of Likes # of Comments Control 2% 1% Test 23% 5% Table V.13 Average engagement per post type # of Likes # of Comments Self-Disclosure 34% 6% Humor 33% 8% Course 6% 2%

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73 Figure V.5 Average Engagement per Course-Based Soci al Network Figure V.6 Average Engagement per Experimental Grou p 0% 5% 10% 15% 20% 25% 30% 35% Control 1 Control 2 Control 3 Control 4 Test 1 Test 2Test 3 Likes Comments 0% 5% 10% 15% 20% 25% Control GroupTest Group Likes Comments

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74 Figure V.7 Average Engagement per Post Type 5.5.3 Control Variables. We then checked for the following control variabl es: Teaching style (online course vs. offline course); the instructor (instructor 1, 2, and 3); time of experiment (spring 2012 vs. fall 2013); and gender (male vs. female). To check for the impact of the control variable “te aching style”, we used multigroup moderation. The dataset was split into two pa rts based on the control variable (teaching style, online course and offline course); we checked the model two times, each time with one of the two sub-samples. We then compa red the results for students in the offline courses, with those in online courses. In b oth groups all of the hypotheses were supported. A t-test was used to check if there are any significant differences between these two results. It shows there are no significa nt differences between the two groups. Thus, there is no concern that the control variable “teaching style” has a significant interaction effect on the model. To check for the impact of the instructor on the re sults, we used multi-group moderation. The dataset was split into three parts based on the instructor; we made three 0% 5% 10% 15% 20% 25% 30% 35% 40% Self-DisclosureHumorCourse # of Likes # of Comments

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75 comparisons. We compared instructor 1 with instruct ors 2 and 3 combined; we compared instructor 2 with instructors 1 and 3 combined; and we compared instructor 3 with 1 and 2 combined. In each one of these comparisons, we ra n the model twice, each time with one of the two sub-samples. In all of the groups al l of the hypotheses were supported. A t-test was used to check if there are any significa nt differences between these two results. It shows there are no significant differences betwe en the two groups. Thus, there is no concern that the instructor has a significant inter action effect on the model. To check for the impact of the control variable “ex periment time”, we used multigroup moderation. The dataset was split into two pa rts based on the control variable (experiment time, spring 2012 and fall 2013); we ch ecked the model two times, each time with one of the two sub-samples. We then compared t he results for students in the spring 2012, with those in fall 2013. In both groups all o f the hypotheses were supported. A ttest was used to check if there are any significant differences between these two results. It shows there are no significant differences betwe en the two groups. Thus, there is no concern that the control variable “experiment time” has a significant interaction effect on the model. To check for the impact of the control variable “ge nder”, we used multi-group moderation. The dataset was split into two parts ba sed on the control variable (gender, male and female); we checked the model two times, e ach time with one of the two subsamples. We then compared the results for male stud ents, with female student. In both groups all of the hypotheses were supported. A t-te st was used to check if there are any significant differences between these two results. It shows there are no significant

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76 differences between the two groups. Thus, there is no concern that the control variable “gender” has a significant interaction effect on th e model.

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77 CHAPTER VI DISCUSSION The results of this study indicate that the hypothe ses model is supported. There is a positive impact for instructor credibility, instr uctor self-disclosure, via a course-based social network, about related topics, e.g. related work experience, and instructor use of appropriate humor, via a course-based social networ k, on student engagement in this course-based social network. There is a negative im pact for instructor self-disclosure, via a course-based social network, about unrelated topi cs, on student engagement in this course-based social network. There is a positive im pact resulting from student engagement in a course-based social network on stud ent motivation to learn, and on student satisfaction with learning. In addition, we found a moderating impact of time s pent in the online social network by student on the relationship between self -disclosure and engagement. Meaning, as the student spends more time interactin g in the online social network, the impact of self-disclosure on engagement will be str onger. We also found a moderating impact of time spent in the online social network b y student on the relationship between humor and engagement. Meaning, as the student spend s more time interacting in the online social network, the impact of humor on engag ement will be stronger. However, we didnÂ’t find a moderating impact of time spent in th e online social network by student on the relationship between credibility and engagement as hypothesized in hypothesis 7c. The results of the two part study are summarized in Table VI.1.

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78 Table VI.1 Summary of Hypotheses Tests. Hypothesis Study Supported H1: Self-Disclosure Course Related Engagement Both Yes H2: Self-Disclosure Course Unrelated Engagement Exploratory Yes H3: Humor Engagement Both Yes H4: Credibility Engagement Main Yes H5: Engagement Motivation Main Yes H6: Engagement Satisfaction Main Yes H7a: Time Spent (Self-Disclosure Engagement) Main Yes H7b: Time Spent (Humor Engagement) Main Yes H7c: Time Spent (Credibility Engagement) Main No This research confirms findings from prior studies, which found that when instructors disclose private information about them selves, like photographs and bibliographies; it positively affects educational o utcomes. However; this study finds the impact differs depending on the type of information that is disclosed. For example, when the instructor posts about work experience related to course related concepts and content it has a completely different effect than when the instructor posts about unrelated personal issues, e.g. the instructorÂ’s beliefs or l ife events and plans. This suggests the later type of information distracts from the academ ic environment of the course. This study also demonstrates that instructor use of humor via a course-based social network group has a positive impact on stude nt engagement in this course-based social network group. This suggests that use of hum or via a social network supports the

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79 instructor-student relationships and removes barrie rs between them. The results of this study also help to clarify contradictory results ab out the impact of using humor in educational environments. While some prior research has found that humor can be used appropriately in the classroom to enhance learning and student perceived learning outcomes (e.g. Gorham and Christophel, 1990), other research has demonstrated a negative impact of humor on learning (e.g., Harris, 1989; Stuart & Rosenfeld, 1994; Torok, McMorris, & Lin, 2004; Ziv, 1988). This stud y demonstrates that use of appropriate humor (humor that conforms to the stand ards outlined in WanzerÂ’s classification) does enhance engagement and hence p erceived educational outcomes when an instructor is communicating with students v ia a course-based social network. These results also demonstrate that the time a stud ent spends in the online social network moderates the impact of communication types used by the instructor (instructor self-disclosure and use of humor) on the student en gagement. When spending more time interacting in the online social network, the stude nt will be more exposed to the instructor posts related to self-disclosure and humor; consequ ently, this studentÂ’s engagement will be more impacted by these posts, compared to anothe r student who spends less time in the online social network. Instructor credibility also has a positive impact o n engagement; credibility brings more reason for students to get engage to begin wit h. However, after spending more time in the online social network the student could figu re out that the instructor is less or more credible. Accordingly, the impact of credibility on engagement could be stronger or weaker depending on the amount of time spent, and w e found it to be weaker; however not significantly weaker. We found that the impact of credibility on engagement is

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80 always significant whether spending low or high amo unt of time in the online social network. 6.1 Theoretical Contribution and Implications to Re search This paper contributes to IS research by deepening our understanding of student engagement in course-based social networks by conce ptualizing an engagement model that supports use of online social network systems for engaging students. The proposed model demonstrates that engagement is an important factor when studying more interactive communication-based systems like online social network systems. Moreover, this research provides new determinants that impact student engagement in a coursebased social network, self-disclosure and use of hu mor. These factors can be utilized by IS researchers to study the antecedents and consequ ences of different types of communication content and understand the relationsh ips between these communication types used via a course-based social network and en gagement in this course-based social network. Results of this study build on the communication pr ivacy management theory (Petronio, 2002) to expand our understanding of typ es of private information that might be the most beneficial and pose the fewest risks in professional settings like college classrooms. Communication privacy management theory stated that there is tension between the decision to disclose or to conceal priv ate information; because selfdisclosure has both benefits and risks. However, th e theory didnÂ’t explain what factors make self-disclosure associated with benefits or wi th risks. This research extended communication privacy management theory by examinin g the impact of self-disclosure type and relevance on the risks and benefits of tha t disclosure. We defined two types of

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81 self-disclosure based on its relevance to the cours e-based social network; self-disclosure related to the course topics, and self-disclosure u nrelated to the course topics. We found that the related self-disclosure is associated with benefits of the this self-disclosure and has a positive impact on engagement, while the unre lated self-disclosure is associated with risks of the this self-disclosure and has a ne gative impact on engagement. This would help to understand how to maximize the benefi ts of self-disclosure, minimize the risks of self-disclosure, and to take the decision the decision to disclose or to conceal private information. The research also contributes to the instructional humor processing theory. It expands our understanding of the instructor use of humor, via a course-based social network, and its impact on the student engagement. The theory stated that the instructor use of appropriate humor, related to the course mat erial, correlates positively with student learning. This research expanded the instructional humor processing theory, by examining the impact of the instructor use of appro priate humor, related to course material, on the student engagement specifically. T his research also expanded the IS theory by utilizing WanzerÂ’s classification of appr opriate and inappropriate instructor use of humor. This assist in confirming that instructor use of humor that we used in this research is the appropriate type of humor. The research also contributes to theory by providin g an engagement model that is unique to online educational setting, by utilizing MooreÂ’s transactional distance theory, to study the moderation impact of time spent by studen t in the online social network. MooreÂ’s transactional distance theory asserts that the physical separation in distance education leads to a potential misunderstandings an d communication gap between the

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82 instructor and the student. However, the theory sta ted that increasing the time spent by student in the online social network decreases this gap. Our research expanded MooreÂ’s transactional distance theory, by finding that the instructor-students communication, via a course-based social network, provides the student w ith the opportunity to spend more time to interact in the online social network; beca use in online setting, students are more likely and have more opportunity to spend more time interacting with the classmates and the instructor than they do in a classroom. That re duces the psychological and physical distance between them and foster psychological clos eness through interactions more than those offered by face-to-face setting. This also br idges the distance between students and the instructor, increasing student engagement. Acco rdingly, we found that increasing the time spent by students in the online social network moderates the impact of instructor use of different communication types (instructor self-d isclosure and use of humor) on engagement. The student perception of the instructo r use of these communication types and its impact on engagement varied just because of the amount time the student spends online. 6.2 Implications to Practice Faculty members in higher education institutions ca n use the results of this research to improve student engagement, and hence, improve studentsÂ’ perceived educational outcomes. This study provided guidance about what content is appropriate to be posted in social networks, like Facebook, and wh at content is not appropriate. In a changing world where the line between social and pr ofessional communication is increasingly blurred, this guidance is essential. F or example, some instructors have been known to "friend" their students via personal socia l network sites like Facebook

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83 (Rutledge, 2011). Results of this study suggest tha t this practice might not be appropriate if the instructor also uses the Facebook profile pa ge to post personal information to family and friends. This research does not suggest that instructors should avoid social network sites like Facebook. Instead, it suggests t hat instructors should create pages or groups targeted to their students and use those pag es or groups to send a clear message to students that they care about them, and they are in terested in fostering a positive relationship with them. When instructors interact with students via Faceboo k, students have the opportunity to use technologies they already use in everyday life, in the classroom. This provides them with new and more accessible resource s to enhance their class knowledge, improve their relationships with their instructors, and positively impact their perceived educational outcomes, like their motivation to lear n and their satisfaction. These research results, about the importance of soc ial contact between instructor and students outside of the classroom (e.g. in Face book groups), has also implications for designers of learning management systems. Designers of learning management systems should try to facilitate posting of content on soci al sharing platforms beside the learning management systems. Other solution could be by supp orting the learning management systems ability to include content directly from th ose social network sites inside the learning management systems itself. This will impro ve the ability of these learning management systems to improve student engagement an d student educational outcomes. 6.3 Limitations and Future Research Three types of online communication have been studi ed as a part of this research: self-disclosure about related work experience, self -disclosure about unrelated personal

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84 issues and the use of humor. However; only two leve ls of each factor have been used, treatment or no treatment (e.g., use humor vs. no h umor). There may be an optimum amount of each type of message to use when communic ating in online social networks, for example, a little humor may improve outcomes, b ut too much may have negative consequences. Future research could replicate this study with more groups and additional factor levels to help capture the impact of differe nt amounts of these factors on student outcomes. The Facebook simulated pages treatment used in the exploratory study may not adequately represent the independent variables effe ct that a longer and deeper experiment can provide. In the exploratory study, engagement i n a course-based social network is measured by asking the participants about their exp ected engagement. However, our longer experiment provided the opportunity for reco rding and measuring the actual engagement in the course-based social network by no ticing the student interaction rate on an actual course-based social network. This guarant eed a higher level of internal validity where the impact on the outcomes measures comes onl y from the treatment factors. This research can also be extended by investigating appropriate types of communication outside the higher-education domain. Today, 90% of firms are using social networks as a part of their online marketing efforts (Stelzner, 2011). Future research can help improve their understanding of ho w to engage their customers around their products, services and brands, and increase c ustomer loyalty. Future research can build on results of this study to increase the conf idence of firms that are not already using online social networks for engaging customers. More over, providing a future model that can be used to understand customer engagement in on line social networks can facilitate

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85 deep and enduring affective bonds between customers and suppliers in the firm-hosted social networks. This can supports a variety of org anizational activities beyond marketing like customer relationship management (CRM), the in novation process for products, and the recruitment of talent for these firms. Finally, communication in virtual teams is another area where this research can be applied. Virtual team leaders typically have less d irect control over team members that are working from different locations. Future resear ch can help the virtual team leader to engage members of the team more in their tasks, and motivate them to be more cooperative. This should allow members to have bett er relationships with each other, which can increase collaboration and improve outcom es for the team. 6.4 Conclusions Online social networks are increasingly being used in different fields. In higher education, students and faculty members have begun to realize the benefits that can be achieved when adopting online social networks like Facebook in the classroom. However, little is known about the types of communi cation that can best be used via an online social network to enhance engagement among m embers of this online social network. This research enhances our knowledge about the use of Facebook in classrooms, by investigating how instructors can use such a tec hnology to engage the students, and advance their perceived educational outcomes. It de monstrates that it is not sufficient to simply communicate with students. How you communica te and what you disclose also has a tremendous impact on student outcomes.

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86 The thesis included two studies. The first study in volves an exploratory study that utilizes a survey to investigate the best combinati on of communication types (among selfdisclosure related interests, self-disclosure unrel ated interests and use of humor) that can be used by instructors via a course-based social ne twork to engage students in this network. The second study involves a real-world exp eriment. In this experiment study, we started with the results from the exploratory st udy about the best combination of communication types that can be used to engage stud ents, we added the instructor credibility that can affect the student engagement, the educational outcomes that can be affected by the student engagement, and the time sp ent by the student in the online social network that moderates the research hypotheses, to the research model in the experiment study. Then we conducted an experiment, where an in structor communicates with students via a real course-based social network for an entire semester. The thesis investigated the communication types use d by the instructor via a course-based social network, because we found that the type of communication has a direct impact on relationships building and develop ment. Building on the literature and the use of course-based social networks in practice we investigated self-disclosure, both related and unrelated to the course, and use of hum or, as types of communication that can be used by instructor when communicating with stude nts via a course based social network. We found that, self-disclosure that is rel ated to the course content, and use of humor, are positively impact the student engagement in a course-based social network. Self-disclosure that is unrelated to the course con tent, however, found to have a negative impact on student engagement. The research also inv estigated the instructor credibility, and its perception in an online setting. Building o n social presence theory, we

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87 hypothesized that instructor credibility has a posi tive impact on student engagement, and this was supported by this research. The thesis investigated student engagement in a cou rse-based social network as the central theme in research surrounding social ne twork use. It also investigated the impact of this engagement on student educational ou tcomes, student motivation to learn, and student satisfaction with learning. These outco mes are traditionally studied as important outcomes in the classroom by instructiona l communication scholars because they are representative of student achievement. The thesis study found that studying engagement in online settings specifically is essential. Relying on the current research about en gagement in face-to-face setting is not sufficient when investigating engagement in online social networks. The main difference between the two settings is the amount of time that the student can spend interacting in the online social network. Accordingly, we investig ated the moderating impact of time spent by student in the online social network. In t his thesis, time spent by student in the online social network was found to significantly mo derate the impact of communication types used by the instructor in a course-based soci al network on student engagement in this network. The impact of instructor self-disclos ure on student engagement found to be significantly stronger for students who spent more time in the online social network comparing to those who spent less time. Similarly, the impact of instructor use of humor on student engagement found to be significantly str onger for students who spent more time in the online social network comparing to thos e who spent less time.

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107 APPENDIX A. Appropriate and Inappropriate Humor Table A1 below represents the categories and subcat egories of appropriate instructor humor. Table A2 represents the categorie s and subcategories of inappropriate instructor humor. Table A1: Categories and Subcategories of Instructo r’s Appropriate Humor. (Wanzer et al., 2006) I. Related Humor. This category included any humor used by the professor that related to the material or enhanced learning in the classroom. Using Media or External Objects to Enhance Learning /Humor attempts that were related to the course material and used props or di fferent types of media to enhance learning. For example, “He regularly dresse d up in costume for theme of class,” “Playing with a slinky to demonstrate a phy sics experiment,” “Used a related cartoon,” or “Showed movies of research tha t were funny because they were outdated.” Jokes/Teacher used jokes that related to the course material. For example, “what’s someone who likes to go out a lot? Answer: Fungi.” Examples/Teacher used humorous examples to illustra te course concepts. For example, “Math teachers have used names in word pro blems that were

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108 Table A1 (con’t.) humorous.” Stories/Teacher used humorous stories to illustrate course concepts or reinforce learning. For example, “Using a funny story about t heir kids, past college experiences, other family members and relating it t o class discussion.” Critical/Cynical/Teacher was critical or cynical ab out course material in an effort to be humorous. For example, “A teacher using of sa rcasm to get a point across,” or “teacher making fun of the book.” College Life Stereotypes/Teacher used humor attempt s related to the course material and targeting stereotypical college behavi ors. For example, “Teacher uses stereotypical behavior, e.g., partying, not st udying, as examples,” “Ask us what types of beer we prefer when they need exam ples to show the demand of things,” or “Using ‘slang’ that students use whe n they are discussing topics.” Directed towards Student/Teasing/Teacher employed h umor attempts related to the material and, at the same time directed towa rds students. For example, “Using a student in a demonstration that was humoro us and harmless.” Teacher Performance/Teacher used humor attempts rel ated to class material that involved some type of animated performance. Fo r example, “A marketing professor runs around the classroom and gets really excited about topics,”

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109 Table A1 (con’t.) “My teacher made a rap about math,” or “Doing the v oice of Columbus while talking about voyages to America.” Role Playing/Activities/Teacher used humor attempts related to course material that involved student role play or activit ies. For example, “Staged events in class that were funny but made a point,” or “we did a skit about what we were learning.” Creative Language Usage/Teacher used humor attempts related to the course material that involved creative language or word pl ay. For example, “Teachers come up with funny mnemonic devices to help us reme mber important material,” or “Talks of bacteria as little beasties or little guys.” II. Humor Unrelated to Class Material. This categor y included any humor used by the professor that did not relate to learning or classr oom enhancement. Stories/Teacher humor attempts that involved storie s that were not related to the class material. For example, “Sometimes teachers wi ll go off on tangents and just tell stories for the heck of it.” Jokes/Teacher humor attempts that involved jokes th at were not related to the course material. For example, “He said that they ar e celebrating 15 years of not killing one another, also known as an anniversary.”

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110 Table A1 (con’t.) Critical/Cynical/Teacher humor attempts that involv ed critical or cynical humor that was not related to the course material. For ex ample, “Poking fun at ignorant behaviors, negative ways of thinking, or other prof essors,” or “General sarcasm.” Directed towards Student/Teasing*/Teacher humor att empts that were not related to the course material and involved teasing or maki ng fun of a student. For example, “my teacher teased a girl in my class abou t a guy she has seen her with.” College Life Stereotypes/Teacher used humor attempt s that were not related to the course material and targeted stereotypical college behaviors. For example, “they have made funny comments on the typical college stu dent (procrastinators, clothing, weekend habits, etc.)” Teacher Performance/Teacher used humor attempts tha t were not related to class material and involved some type of animated perform ance. For example, “Making faces at the class,” or “Jumped up on desk and star ted acting like a monkey.” Creative Language Usage/Teachers used humor attempt s that were not related to the course material and involved creative language or word play. For example, “Teachers using puns,” or “Plays on words which are humorous.” Current Events/Political*/Teachers used humor attem pts that were not related to the course material and involved current events or politics. For example, “He brings in current issues in the world and finds hum or out of them.”

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111 Table A1 (con’t.) Using Media or External Objects*/Humor attempts tha t were not related to the course material and involved the use of props or di fferent types of media to enhance learning. For example, “Showing pictures of funny things,” or “He likes to play random assortments of music before class.” III. Self-Disparaging Humor. This type of humor inv olves jokes, stories or comments in which an instructor criticizes, pokes fun of or bel ittles himself/herself. Make Fun of Himself/Herself: Humor attempts targeti ng the teacher in a general way. For example, “A teacher making fun of himself. ” Make Fun of Personal Characteristics: Humor attempt s targeting personal characteristics of the teacher. For example, “When a teacher joked about his eyesight and clumsiness.” Tell Embarrassing Stories: Teacher shares embarrass ing stories in an attempt to be funny. For example, “Teacher telling life storie s that may have been embarrassing for them, or put them in an awkward si tuation.” Make Fun of Mistakes Made in Class: In an attempt t o be funny the teacher makes fun of a mistake he/she made. For example, “P oking fun at themselves for a mistake they have made in class.” Make Fun of Abilities in an attempt to be funny the teacher might make fun of

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112 Table A1 (con’t.) his/her abilities. For example, “Teachers often ref er to themselves as stupid.” IV. Unintentional or Unplanned Humor. The teacher d id not intend to be funny, but the students found his/her behavior to be humorous. Exa mples: Unintentional puns and slips of the tongue. Table A2: Categories and Subcategories of Instructo r’s Inappropriate Humor. (Wanzer et al., 2006) I. Offensive Humor. Humor in this category included any types of humor that were clearly identified as offensive in nature and not n ecessarily targeted at a specific person or persons. Sexual Jokes/Comments*/Teacher tells sexual jokes o r makes sexual comments in an attempt to be humorous. For example, ‘‘I had a h ealth class in which the teacher would make graphic jokes about sex.’’ Vulgar Verbal and Nonverbal Expressions*/Teacher us es vulgar verbal or nonverbal expressions. For example, ‘‘Swearing,’’ ‘ ‘Flipping the bird to students in class,’’ or ‘‘Carrying or wearing something that is derogatory.’’ Drinking*/ In an attempt to be funny, the teacher w ill make references to drinking or alcohol. For example, ‘‘When a teacher talks abo ut getting drunk,’’ or ‘‘I find it offensive when professors always use examples pe rtaining to alcohol.’’

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113 Table A2 (con’t.) Inappropriate Jokes*/Teacher tells inappropriate jo kes in class. For example, ‘‘Teachers crack jokes that do not relate to the le sson,’’ or ‘‘My English teacher told a few inappropriate jokes.’’ Personal Life*/In an attempt to be funny, the teach er tells stories about his/her personal life. For example, ‘‘Teacher always told s tories about herself, son, and dog in the middle of lectures. It was basically a w aste of time.’’ Drugs/Illegal Activities*/Teacher humor attempts th at involved discussion of drugs or illegal activities. For example, ‘‘Talking about inappropriate things such as pornography and drugs.’’ Morbid Humor*/Teacher humor attempts that involve d iscussions about death or another related morbid topic. For example, ‘‘In a l aw class, professor tells cases of when people died or got hurt in a humorous manne r.’’ Sarcasm*/Teacher humor attempts that involve sarcas m. For example, ‘‘When we asked him how to do a problem he would say somethin g such as ‘with a pencil’.’’ II. Disparaging Humor Student Target. Humor in this category is clearly disparaging in nature and targets students as a group or individua l students. Students (as a group) Nonspecific Response*/Teacher humor attempts that t argeted students in a nonspecific way. For example, ‘‘Jokes that spoke ab out all students in general and made fun of them.’’

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114 Table A2 (con’t.) Based on Intelligence*/Teacher humor attempts that targeted students’ intelligence. For example, ‘‘Teacher referred to a group of students as ‘the living brain dead.’’’ Based on Gender*/Teacher humor attempts that target ed students based on gender. For example, ‘‘One teacher actually advised girls to take home education instead of physical education.’’ Based on Appearance*/Teacher humor attempts that ta rgeted students’ appearance. For example, ‘‘A professor making refer ence to the number of students that wear clothes from Abercrombie & Fitch .’’ One Student (singled out) Nonspecific Response*/Teacher humor attempts that t argeted a single student in a nonspecific way. For example, ‘‘Anytime when a teac her puts another student down in front of others just to get a laugh from th e class.’’ Based on Intelligence*/Teacher humor attempts that target a specific student’s intelligence. For example, ‘‘Calling someone stupid in a humorous way,’’ or ‘‘Making fun of a student’s answer, even though the student was serious about it.’’ Based on Student’s Personal Life/Opinions/Interests */Teacher humor attempts that target a specific student’s personal life, opi nions or interests. For example, ‘‘A comment made to demean someone who has expresse d their opinion,’’ or ‘‘Making fun of a student’s personal life.’’

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115 Table A2 (con’t.) Based on Appearance*/Teacher humor attempts that in volved targeting a specific student’s appearance. For example, ‘‘A particular t eacher would personally attack people by making fun of their clothes or the way th ey looked.’’ Based on Gender*/Teacher humor attempts that involv ed targeting a specific student based on gender. For example, ‘‘Teacher mad e a very sexual comment in class towards a female and then laughed.’’ Based on Religion*/Teacher humor attempts that targ eted a specific student based on religion. For example, ‘‘The student was of Indi an decent and a practicing Hindu. The teacher mocked her by saying, ‘Go worshi p your cow’.’’ III. Disparaging Humor: ‘‘Other’’ Target. Humor att empts in this category are clearly disparaging in nature, and are targeted at individu als or groups other than students. Using stereotypes in general*/Teacher humor attempt s that involved use of stereotypes in a general way. For example, ‘‘Excess ive use of stereotypes in jokes.’’ Targeting Gender Groups*/Teacher humor attempts tha t involved targeting males or females. For example, ‘‘Our teacher someti mes stereotypes certain sexes and makes jokes about them.’’ Targeting Ethnic or Racial Groups*/Teacher humor at tempts that involved targeting particular racial or ethnic groups. For e xample, ‘‘I have a teacher that regularly makes fun of different ethnic/cultur al groups,’’ or ‘‘A teacher would make generalizations about a race, and make f un of that race in class.’’

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116 Table A2 (con’t.) Target is University Related*/Teacher humor attempt s that involved targeting university staff. For example, ‘‘Making fun of othe r teachers,’’ or ‘‘Making fun of certain organizations at the school.’’ Targeting Religious Groups*/Teacher humor attempts that involved targeting certain religions groups. For example, ‘‘Several pr ofessors have made references to religion, especially Christianity, in belittling terms.’’ Targeting persons of a given sexual orientation*/Te acher humor attempts that involved targeting people based on sexual orientati on. For example, ‘‘Making fun of sexual orientation,’’ or ‘‘Jokes referring t o gays.’’ Targeting persons of a given appearance*/Teacher hu mor attempts that involved targeting people based on their appearance For example, ‘‘Telling blonde jokes.’’ Political motivation*/Teacher humor attempts that i nvolved targeting people based on their political affiliations. For example, ‘‘Humor which is politically motivated, therefore projecting their views upon yo u.’’ IV. Self-Disparaging Humor. This type of humor invo lves a professor criticizing, poking fun of or belittling himself/herself. Example: Prof essor says, ‘‘I am such an idiot!’’ to the class or performs a similar self-disparaging.

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117 B. Examples of Facebook Pages used in the Study B.1 A sample simulated Facebook page used in the ex ploratory study ISMG2050 When I was working as analyst at Oracle; I used tools for solving business problems similar to the one weÂ’ll use in this class Like. Comment. Share. ISMG2050 The school of business is preparing for a workshop about using Microsoft Excel in business. In my opinion, itÂ’s important for students to attend such events related to their courses. I recommend all of you to attend it Like. Comment. Share. ISMG2050 Next class weÂ’ll discuss linking data between multi ple Excel files. I used to use this feature in my work so I could keep separate files for different topics, but still I could use data from one Excel file in another one

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118 when needed Like. Comment. Share. ISMG2050 So what’s involved in making reports look good? A good executive summary and charts. Charts are visual and easy to read. They are an effective way to communicate data Like. Comment. Share. ISMG2050 One of the main tasks I was responsible for in my career was to keep track of all transactions that occur in the business. I used to use Excel to creat e a “journal” file; we’ll discuss this in the next clas s Like. Comment. Share. ISMG2050 In Excel we can use the format features to highligh t important data, like tax rates, by using a specific text color, filling, or boarders Like. Comment. Share. ISMG2050 We’ll start to use the MS Access next class please be prepared Like. Comment. Share. ISMG2050 The slides for the next module have been uploaded

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119 Like. Comment. Share. ISMG2050 One of the most common uses of Microsoft Access in business is to keep track of customer information: name, address, and phone numbers Like. Comment. Share. ISMG2050 For assignment 5, youÂ’ll need to start with the Exc el file: assignment5.xlsx, you can find it under data files on blackboard Like. Comment. Share. ISMG2050 The assignments solutions have been posted, itÂ’ll b e good to review them for the test Like. Comment. Share. ISMG2050 A study guide for the exam has been downloaded under data files on blackboard Like. Comment. Share. There are no more posts to show.

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120 B.2 A screenshot of a real Facebook page used in th e main study Figure B.1 a screenshot of a real Facebook page use d in the main study

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121 C. Instructor Self-Disclosure Scale (Cayanus and Martin, 2008) First: Self-disclosure related course interest. In the exploratory study: Think of the Facebook cou rse page that you just explored. Suppose that you are taking this specific course with this specific instructor who created this Facebook page for her course to co mmunicate with her students. Then, indicate your level of agreement with the following statements as they relate to YOUR INSTRUCTOR on a 1 to 7 scale with 1=Completely Disa gree and 7=Completely Agree. In the main study: Think of the Facebook course pag e, ISMG2050, which was created by your instructor in this course to commun icate with you. Indicate your level of agreement with the following statements as they rel ate to YOUR INSTRUCTOR on a 1 to 7 scale with 1=Completely Disagree and 7=Complet ely Agree. Completely Disagree 1 2 3 4 5 6 7 Completely Agree 1. My instructor often posts her opinions about curren t course related events 2. My instructor often posts about her attitudes towar d course related events occurring on campus 3. My instructor often posts her opinion about course related events in the community 4. My instructor often shares her dislikes and likes r elated to the course content 5. My instructor reveals relevant work experience in h er posts

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122 Second: Self-disclosure unrelated course interest. In the exploratory study: Please think of the Faceb ook course page that you just explored. Suppose that you are taking this specific course with this specific instructor who created this Facebook page for her course to co mmunicate with her students. Then, indicate your level of agreement with the following statements as they relate to YOUR INSTRUCTOR on a 1 to 7 scale with 1=Completely Disa gree and 7=Completely Agree. Completely Disagree 1 2 3 4 5 6 7 Completely Agree 1. My instructor often posts her opinions about curren t course events unrelated to the course 2. My instructor often posts about her attitudes towar d course unrelated events occurring on campus 3. My instructor often posts her opinion about course unrelated events in the community 4. My instructor often shares her dislikes and likes u nrelated to the course content My instructor reveals personal information about he rself in her posts

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123 D. Instructor Use of Humor Scale (Frymier, Wanzer and Wojtaszczyk, 2008) In the exploratory study: Please think of the Faceb ook course page that you just explored. Suppose that you are taking this specific course with this specific instructor who created this Facebook page for her course to co mmunicate with her students. Then, indicate your level of agreement with the following statements as they relate to YOUR INSTRUCTOR on a 1 to 7 scale with 1=Completely Disa gree and 7=Completely Agree. In the main study: Please think of the Facebook cou rse page, ISMG2050, which was created by your instructor, “Instructor’s name” in this course to communicate with you. Then, indicate your level of agreement with th e following statements as they relate to YOUR INSTRUCTOR on a 1 to 7 scale with 1=Complet ely Disagree and 7=Completely Agree. Completely Disagree 1 2 3 4 5 6 7 Completel y Agree 1. My instructor posts humor related to course materia l 2. My instructor posts funny props to illustrate a con cept or as an example 3. My instructor posts jokes related to course content 4. My instructor posts humorous story related to cours e content 5. My instructor uses language in her posts in creativ e and funny ways to describe course material 6. I found that the humor used by the instructor detra ct from the course experience 7. The type and amount of humor used by this instructo r encourages me to interact (comments/likes) on this Facebook page

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124 E. Student Engagement Scale (Webster & Ho, 1997) In the exploratory study: Please think of the Faceb ook course page that you just explored. Suppose that you are taking this specific course with this specific instructor who created this Facebook page for her course to co mmunicate with her students. Then, indicate your level of agreement with the following statements as they relate to the Facebook page on a 1 to 7 scale with 1=Completely D isagree and 7=Completely Agree. In the main study: Please think of the Facebook cou rse page, ISMG2050, which was created by your instructor, “Instructor’s name” in this course to communicate with you. Then, indicate your level of agreement with th e following statements as they relate to the Facebook page on a 1 to 7 scale with 1=Compl etely Disagree and 7=Completely Agree. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 1. This Facebook page kept me totally absorbed in the browsing 2. This Facebook page held my attention 3. This Facebook page excited my curiosity 4. This Facebook page was fun 5. This Facebook page was intrinsically interesting 6. This Facebook page was engaging

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125 F. Instructor Credibility Scale (Teven and McCroskey’s, 1997) In the main study: Please think of your instructor, “Instructor’s name”, who created the Facebook course page, ISMG2050, in this course to communicate with you. Then, Please circle the number toward either level of your agreement regarding your instructor. 1. Unintelligent 1 2 3 4 5 6 7 Intelligent 2. Inexpert 1 2 3 4 5 6 7 Expert 3. Incompetent 1 2 3 4 5 6 7 Competent 4. Uninformed 1 2 3 4 5 6 7 Informed 5. Stupid 1 2 3 4 5 6 7 Bright 6. Trained 1 2 3 4 5 6 7 Untrained

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126 G. Student Motivation to Learn Scale (Richmond, 1990) In the main study: Please think of the Facebook cou rse page, ISMG2050, which was created by your instructor, “Instructor’s name” in this course to communicate with you. Then, circle the number toward either level of your feelings about learning the content in the class. Motivated 1 2 3 4 5 6 7 Unmotivated Interested 1 2 3 4 5 6 7 Uninterested Involved 1 2 3 4 5 6 7 Uninvolved Excited 1 2 3 4 5 6 7 Not Excited Dreading it 1 2 3 4 5 6 7 looking forward to it

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127 H. Student Satisfaction with Learning Scale (Frymier & Houser, 1998) In the main study: Please think of the Facebook cou rse page, ISMG2050, which was created by your instructor, “Instructor’s name” in this course to communicate with you. Then, circle the number toward either level of your feelings about learning the content in the class. Pleased 1 2 3 4 5 6 7 Displeased Satisfied 1 2 3 4 5 6 7 Dissatisfied Content 1 2 3 4 5 6 7 Discontent